radio resource management wcdma systems
TRANSCRIPT
ABSTRACT
SUBRAMANIAM, KAMALA. Radio Resource Management in UMTS-WCDMA Systems.
(Under the direction of Professor Arne A. Nilsson).
Universal Mobile Telecommunications System (UMTS) is a Third Generation (3G)
cellular technology representing an evolution of a heterogenous mix of services and increased
data speeds from today’s second generation mobile networks. UMTS uses Wideband Code
Division Multiple Access (WCDMA) as its radio air interface. The implementation of
WCDMA is a technical challenge because of its complexity and versatility. Billions of dol-
lars have been spent procuring these air interfaces. To exploit the flexibility of the air
interface, development of ‘Radio Resource Management (RRM)’ schemes are imperative.
RRM is comprised of power control, handover control, load control and resource allocation
algorithms. These ensure optimum network coverage, maximize the system throughput and
, guarantee Quality of Service (QoS) requirements to users having different requirements.
This research investigates mainly the resource allocation and power control algo-
rithms with which the load control and handover control are intertwined. The state of the
art is studied and their pros and cons are discussed, which lays the foundation for the need
for more efficient RRM schemes that are eventually presented in this research.
The two main schemes considered here are:1)Adaptive Call Admission Control
(ACAC) scheme for resource allocation where the system is mathematically modeled as a
multi-rate system with priority. Further, a tier based analytical model pertaining to the
hierarchical hexagonal cell structure is analyzed and mobility is given importance. 2) Adap-
tive Uplink Power Control (AUPC) scheme for power control is analyzed where Monte Carlo
simulations are used to fine-tune WCDMA link budget parameters. Finally, Location Up-
date (LU) procedures in cellular networks using Bloom Filters is studied where bandwidth
gain is given importance.
Various performance metrics are observed and two key metrics are given the most
importance: the Call Blocking and Call Dropping probabilities. Simulation results are com-
pared to the existing schemes and further strengthened by comparing them to analytical
results which validate the entirety of this research.
RADIO RESOURCE MANAGEMENT IN UMTS-WCDMA SYSTEMS
by
Kamala Subramaniam
A dissertation submitted to the Graduate Faculty ofNorth Carolina State University
in partial satisfaction of therequirements for the Degree of
Doctor of Philosophy
Computer Engineering
Raleigh, NC
2005
Approved By:
Dr. George Rouskas Dr. Wenye Wang
Dr. Arne A. Nilsson Dr. Ioannis ViniotisChair of Advisory Committee
ii
For Piyush......whose existence is testimony to life’s goodness
iii
Biography
Kamala Subramaniam was born to Gauri (mother) and Mani (father) in India on sev-
enteenth February, 1977. She spent the first ten years of her life in Mumbai (formerly
Bombay), the financial capital of India and the next twelve in Bangalore, the silicon valley
of India.
After her high school, she joined Vishweshwariah Techological University for her
Bachelors in Electronics and Instrumentation Engineering where she graduated summa cum
laude in 1998. She then enrolled for a Masters at North Carolina State University in the
department of Electrical and Computer Engineering majoring in Computer Networking.
What she learnt here coupled with the height of the telecom bubble, whet her curiosity and
sealed the deal with the world of telecommunications.
She went to work at Nortel Networks (NTL) at Research Triangle Park as a VoIP
software developer for a year. Working with the finest people in the area, she realized the
need to hone her skills and joined the Doctoral program at North Carolina State University
in the same department. Also, wireless networking was taking off in a huge way.
The next four years, her most fruitful professionally, she developed algorithms
for cellular networks. She also interned with Catapult Communications (CATT) a third
generation solutions provider. In the interim, she was the President of the Electrical and
Computer Engineering Graduate Student Association (ECEGSA) where she introduced the
seminar series, semester picnics and more faculty-student interaction socials. She also served
as the Vice-President of the Indian Graduate Students Association. She was honored to
be accepted as a member of Eta Kappa Nu, the Electrical Engineering Honors Society and
Society of Women Engineers.
She hopes to continue to work in research areas involving cellular networks, per-
formance modeling, queuing theory and random processes.
iv
Acknowledgements
This dissertation would not have been possible but for Dr. Arne Nilsson. I am
grateful to him for being a mentor first and then an advisor. He has helped me rise for
every fall I have had both professionally and personally. The vast knowledge he granted me
will carry me through the rest of this life with much panache.
I am grateful to Dr. Trussell (the Director of Graduate of Programs) and his
efficient department, for helping me with the day to day saga of being an international
graduate student. I thank Dr. Viniotis, Dr. Rouskas and Dr. Wang for their guidance and
I am honored to have them on my committee.
I am grateful to my mother, Gauri, for making me the fighter I am today and to my
sister, Priya, for her unwavering confidence in and love for me. This would be incomplete
without my friends. Ramki, who helped me with my last minute, late night coding issues
and his unconditional friendship. Reshmi and Sreekanth, who gave me tremendous moral
support. And Piyush, for always being there.
v
Contents
List of Figures viii
List of Tables x
1 Introduction 11.1 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.2 Specific Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.3 Background and Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . 51.4 Research Questions and Limitations . . . . . . . . . . . . . . . . . . . . . . 6
2 Background 92.1 Architecture of the UMTS system . . . . . . . . . . . . . . . . . . . . . . . 92.2 Wideband Code Division Multiple Access (WCDMA) . . . . . . . . . . . . 122.3 UMTS QoS Bearer Classes . . . . . . . . . . . . . . . . . . . . . . . . . . . 142.4 Rationale behind CAC schemes . . . . . . . . . . . . . . . . . . . . . . . . . 162.5 Terminology used in CAC schemes . . . . . . . . . . . . . . . . . . . . . . . 18
3 State of the Art 203.1 Before 3G and WCDMA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203.2 On the capacity of CDMA and WCDMA systems . . . . . . . . . . . . . . . 213.3 On WCDMA and UMTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.3.1 Dimitriou et. al. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223.3.2 Capone et. al. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233.3.3 Stol et. al. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243.3.4 Victor O.K. Li et. al . . . . . . . . . . . . . . . . . . . . . . . . . . . 253.3.5 Schultz et. al. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
4 Methodology and Model Design 314.1 Wideband Power Based Admission Control Scheme . . . . . . . . . . . . . . 32
4.1.1 Uplink Criterion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 324.1.2 Downlink Criterion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
4.2 Throughput Based Admission Control Scheme . . . . . . . . . . . . . . . . 35
vi
4.2.1 Uplink Criterion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 354.2.2 Downlink Criterion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
4.3 Proposed Adaptive Call Admission Control Scheme . . . . . . . . . . . . . . 36
5 Simulation Modeling 385.1 Node-B Simulation Parameters . . . . . . . . . . . . . . . . . . . . . . . . . 385.2 Radio Network Controller Call Admission Control Simulation Parameters . 405.3 WCDMA Link Budget . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 405.4 Voice Users’ Simulation Parameters . . . . . . . . . . . . . . . . . . . . . . . 435.5 Video Users’ Simulation Parameters . . . . . . . . . . . . . . . . . . . . . . 435.6 FTP Users’ Simulation Parameters . . . . . . . . . . . . . . . . . . . . . . . 435.7 Mobility Simulation Parameters . . . . . . . . . . . . . . . . . . . . . . . . . 44
6 Analytical Modeling 456.1 Multi-rate Erlang-B Computation . . . . . . . . . . . . . . . . . . . . . . . 456.2 Single rate prioritized system using conservation law . . . . . . . . . . . . . 486.3 Proposed Analytical Models . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
6.3.1 Model 1: Multi-rate Erlang-B with priority . . . . . . . . . . . . . . 506.3.2 Model 2: Multi-rate Erlang-B with priority and tier analysis . . . . 51
7 Power Control 537.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 537.2 Problem Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 547.3 Previous Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 577.4 Step Size Evaluation of Eb/No . . . . . . . . . . . . . . . . . . . . . . . . . . 59
7.4.1 In Outer Loop Power Control . . . . . . . . . . . . . . . . . . . . . . 597.4.2 In Adaptive Uplink Power Control . . . . . . . . . . . . . . . . . . . 60
7.5 Spectral Efficiency of a WCDMA cell . . . . . . . . . . . . . . . . . . . . . . 617.6 Adaptive Calculation of Pj . . . . . . . . . . . . . . . . . . . . . . . . . . . 637.7 Simulation Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 647.8 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
8 Results and Discussions 658.1 Call Admission Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
8.1.1 Single Run Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . 678.1.2 Confidence Intervals . . . . . . . . . . . . . . . . . . . . . . . . . . . 678.1.3 Comparison of analytical and simulation results . . . . . . . . . . . . 718.1.4 Comparison of Simulation and Analytical Results with Tier Analysis 72
8.2 Power Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 748.2.1 Comparison of OLPC and AUPC with respect to Average (Eb/No)j 748.2.2 Comparison of OLPC and AUPC with respect to Total ηUL . . . . 758.2.3 Comparison of OLPC and AUPC with respect to Noise Rise . . . . 758.2.4 Comparison of OLPC and AUPC with respect to (Eb/No)j . . . . . 768.2.5 Comparison of OLPC and AUPC with respect to Lj . . . . . . . . . 778.2.6 Comparison of OLPC and AUPC with respect to Transmit Power Pj 77
vii
8.2.7 Comparison of Voice and Data Blocking Probabilities with and with-out Power Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
9 Location Updates of Cellular Networks Using Bloom Filters 809.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
9.1.1 Bloom Filters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 819.1.2 Variations of Bloom Filters . . . . . . . . . . . . . . . . . . . . . . . 819.1.3 Applications of Bloom Filters . . . . . . . . . . . . . . . . . . . . . . 83
9.2 Location Updates and Bloom Filters . . . . . . . . . . . . . . . . . . . . . . 849.3 Analytical Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
9.3.1 Optimization of Hash Functions (OBF) . . . . . . . . . . . . . . . . 869.3.2 Cumulative Bloom Filters . . . . . . . . . . . . . . . . . . . . . . . . 889.3.3 Performance Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
9.4 Simulation Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 909.5 Results and Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
9.5.1 Without Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . 919.5.2 With Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 929.5.3 With Optimization and Cumulative Bloom Filters . . . . . . . . . . 93
9.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
10 Conclusions and Future Work 97
Bibliography 100
A Acronyms 108
viii
List of Figures
1.1 Research Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.1 UMTS Network Architecure . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
4.1 Shows Load curve and the, due to a new call, increase in Interference . . . . 32
5.1 Simulation Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
6.1 Multi-rate Erlang-B . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 466.2 Part 1: Analytical Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . 506.3 Part 2: Analytical Modeling with tiers . . . . . . . . . . . . . . . . . . . . 52
7.1 Near Far Effect in WCDMA . . . . . . . . . . . . . . . . . . . . . . . . . . . 557.2 Outer Loop Power Control in WCDMA . . . . . . . . . . . . . . . . . . . . 57
8.1 Comparison of Data, Voice and Total Blocking Probabilities of 3 schemes . 688.2 Comparison of Data, Voice and Total Blocking Probabilities of 3 schemes . 708.3 Comparison of Data, Voice and Total Dropping Probabilities of 3 schemes . 708.4 Comparison of Analytical and Simulation Results . . . . . . . . . . . . . . . 718.5 Comparison of Data Blocking with and without Tier Analysis . . . . . . . . 738.6 Comparison of Voice Blocking with and without Tier Analysis . . . . . . . . 738.7 OLPC and AUPC with respect to Average (Eb/No)j . . . . . . . . . . . . . 748.8 OLPC and AUPC with respect to Total ηUL . . . . . . . . . . . . . . . . . . 758.9 OLPC and AUPC with respect to Noise Rise . . . . . . . . . . . . . . . . . 768.10 OLPC and AUPC with respect to (Eb/No)j . . . . . . . . . . . . . . . . . . 778.11 OLPC and AUPC with respect to Lj . . . . . . . . . . . . . . . . . . . . . . 788.12 OLPC and AUPC with respect to Pj . . . . . . . . . . . . . . . . . . . . . 788.13 Comparison of Voice and Data Blocking Probabilities with and without
Power Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
9.1 Location Request and Location Update . . . . . . . . . . . . . . . . . . . . 859.2 Optimization of Bloom Filter . . . . . . . . . . . . . . . . . . . . . . . . . . 879.3 Cumulative Bloom Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
ix
9.4 False Positives without Optimization . . . . . . . . . . . . . . . . . . . . . 929.5 Gain without Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . 939.6 False Positives without Optimization . . . . . . . . . . . . . . . . . . . . . 949.7 Comparison of Analytical and Simulation Results . . . . . . . . . . . . . . 949.8 False Positives with Optimization and CBF . . . . . . . . . . . . . . . . . . 959.9 Gain with Optimization and CBF . . . . . . . . . . . . . . . . . . . . . . . 96
x
List of Tables
2.1 UMTS QoS Classes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
5.1 WCDMA Link Budget of 30 kbps Voice Service . . . . . . . . . . . . 41
8.1 Confidence Interval for Total Blocking Probability . . . . . . . 698.2 Confidence Interval for Total Dropping Probability . . . . . . . 69
1
Chapter 1
Introduction
The future telecommunications networks, such as the third-generation (3G) wire-
less networks, aim to provide integrated services such as voice, data, and multimedia via
inexpensive low-powered mobile computing devices over wireless infrastructures [1]. Today,
consumers use the Internet to access information. The next logical step should be to enable
users to do the same on the move. That is providing mobility.
European Telecommunications Standards Institute (ETSI) within the Interna-
tional Telecommunication Union’s (ITU’s) International Mobile Telecommunications (IMT)
2000 framework has developed Universal Mobile Telecommunications Systems (UMTS)
as a solution to the future broadband multimedia wireless networks in association with
3GPP (Third Generation Partnership Project). UMTS provides data up to 2 Mbps making
portable videophones a reality. UMTS seeks to build on and extend the capability of today’s
mobile, cordless and satellite technologies by providing high capacity, data capability and
a far greater range of services using an innovative radio access scheme and an enhanced,
evolving core network. UMTS allows us to be connected all the time so there is no time
wasted with dialing up and logging on, instead we automatically receive email and applica-
tion data while online. UMTS speeds up the convergence between telecommunications, IT,
media and content industries. It provides low-cost, high-capacity mobile communications
with global roaming capabilities.
Billions of dollars have been spent in procuring the UMTS licenses. It is thereby
important to ensure that these resources are used efficiently. To support maximum number
of users per unit resource, Radio Resource Management (RRM) schemes has been a widely
2
researched topic. RRM algorithms are responsible for efficient utilization of the air interface
resources. RRM is need to guarantee QoS, to maintain the planned coverage area and to
offer high capacity. The family of RRM algorithms can be divided into handover control,
power control, admission control, load control and packet scheduling functionalities. RRM
schemes has been a wide area of research to support increasing demands of consumers
to want information in all forms, i.e., voice, video, pictures, music, and text etc in these
heterogeneous UMTS networks while providing Quality of Service (QoS) guarantees.
This chapter defines the problem studied, investigated and analyzed in this re-
search study. The remainder of this chapter is organized as follows. Section 1.1 states the
research problem under investigation. The specific contribution in this research in men-
tioned in Section 1.2. A brief background and the motivation are presented in Section
1.3. The questions posed for this research study and their limitations and drawbacks are
presented in Section 1.4.
1.1 Problem Statement
In UMTS systems, the coverage area is divided into hexagonal cells. Each cell has
a limited set of resources provided by the air interface it uses such as CDMA (Code Division
Multiple Access), WCDMA (Wide-band CDMA), EDGE (Enhanced Data rates for GSM
(Global System for Mobile communication) Evolution) etc. These resources are shared by
a number of users running different applications such as voice, video, FTP (File Transfer
Protocol), HTTP (Hyper Text Transfer Protocol). Listed below in order of importance and
focus of research; the problem statement is defined.
Before admitting a new mobile, call admission control needs to check that the ad-
mittance will not sacrifice the planned coverage area or the QoS of the existing connections.
Admission control should allocate resources effectively by accepting and rejecting calls as
appropriate. The admission control algorithm estimates the load increase that the accep-
tance of the call would cause in the radio network. This has to estimated separately for
the uplink and the downlink directions. The requesting call can be admitted only if both
the uplink and downlink requirements are met. This bi-directional call admission control
3
or resource allocation algorithms are important.
Mainly two types of calls are sharing these resources or channels. The new calls
and the calls in progress. Clearly, from the users point of view, it is more annoying for a
call being forced to terminate rather than it being blocked at start. Hence calls in progress
needs to be given higher priority then new calls.
In heterogenous systems, service requirements are different and can also be nego-
tiated. For example, voice users require low bit-rates but have very low tolerance to delay
and data users require higher bit-rates but may have a higher tolerance to delay. Thus, QoS
needs to be guaranteed to each traffic class of calls in terms of either bandwidth, delay, end
user throughput, blocking and dropping probabilities etc.
To top it all mobility poses severe complications. Handovers are needed in cellular
systems to handle movement of mobiles across cell boundaries. This influences the resources
not only in the cell under consideration, but also in the neighboring cells.
Power Control is needed to reduce near-far effects where one mobile that is close to
the base station transmitting at a high power can effectively block out all the other mobiles
in the same cells by increasing the interference in the system to unacceptable limits. By
controlling the emitting powers of each mobile and that of the base-station, interference can
be reduced and hence capacity increased.
It is evident now that good resource management schemes is a must in 3G systems.
1.2 Specific Contribution
The specific contribution of this research is to identify Radio Resource Management
as a combination of algorithms. Existing algorithms are built upon and improved in addition
to innovating and implementing new techniques. The study also shows how a culmination
of these new algorithms in each area presented in this research work together to form an
efficient overall RRM scheme.
Specifically, this research is divided into two RRM algorithms. Chapters 4, 5 and
6 deal with Resource Allocation or Call Admission Control which forms a major portion of
this research. Chapter 7 deals with Power Control.
4
This research proposes an Adaptive Call Admission Control (ACAC) scheme that
augments the existing prevalent CAC schemes to perform much better than any that is
currently deployed. The performance was judged based on two important QoS parameters:
New Call Blocking Probability and Handoff Call Dropping Probability.
For the simulation model, the Radio Network Controller (RNC) from the OPNETTM
model library was augmented to add and improve functionalities related to radio resource
management.
A lot of work has been done by analyzing multi-rate systems running heterogeneous
traffic as in UMTS. However, considering priority has been a very complex problem. Work
has also been done on single-rate systems with priority. For the analysis, we model this
UMTS network as a multi-rate system with priority.
In addition to modeling this as a multi-rate system with priority, we also analyze
the tier-based cellular structure of UMTS systems and the effects of handoff from the
neighboring tier to the cell under consideration.
In the power control area, it was identified that a number of WCDMA air interface
parameters can influence the performance of this algorithm. This research introduces the
concept of fine-tuning certain Power Control parameters and then adaptively choosing the
transmit power of the UE to increase the spectral efficiency of the WCDMA system, which
is an expensive air interface. The advantage of such a scheme is the simplicity of fine-tuning
and Monte Carlo simulations. The contribution in the power control area is the introduction
of Adaptive Uplink Power Control (AUPC) scheme which adheres to 3GPP specifications.
Eventually, this research introduces the concept of Bloom Filters and their various
applications, specifically those in cellular networks. The FCC mandated that carriers using
handset-based wireless location systems must provide the location of 911 calls to appropriate
Public Safety Answer Points (PSAPs) and be accurate to within 50 meters 67 percent of the
time and to within 150 meters 95 percent of the time. This research identified that though
not much work has been done in this area, there is a good potential for the same and applied
hash paging using Bloom Filters to observe the improvement in bandwidth gain. The goal
of this research, which was to see an exponential improvement in bandwidth while keeping
the false positives to a realistic minimum, was obtained by applying the optimization and
5
cumulative bloom filter schemes.
1.3 Background and Motivation
The early cellular networks were systems where resources were finite. For example,
time slots in TDMA (Time Division Multiple Access) systems and frequency slots in FDMA
(Frequency Division Multiple Access) systems. In such cases capacity planning was not a
very cumbersome task because the number of available channels per sector or cell was
fixed. Call admission control schemes in such systems involved the management of these
hard limited number of channels. Channels were allotted by fair allocation by assessing
the system from the present cell and its neighboring cells. Priority was mostly given by
allotting a fraction of channels to higher priority traffic classes. This was popularly called
the guard-channel scheme [2], [3], [4].
In the case where the air interface is WCDMA, there is no absolute hard upper
limit on the number of users that can be supported per sector or cell. This is because CDMA
systems have a frequency reuse factor of one. CDMA systems are interference limited with
soft capacity. In addition to capacity, the Signal-to-Interference Ratio (SIR) forms the
basis for call admission control that has been studied for years. The 3GPP has classified
bearer services of UMTS into four different QoS traffic classes according to different QoS
requirements of bandwidth, delay etc. How to implement CAC schemes to UMTS systems
having these bearer classes has been a topic of much interest.
The motivation behind this work was achieved by studying the prevalent CAC
schemes that exist in UMTS systems. It was observed that these schemes were preferential
in their treatment to certain QoS traffic classes. The need for a CAC scheme that would
eliminate or minimize this preferential treatment was recognized and is the drive behind
this research study.
After efficient CAC schemes were developed, in the upper layers of the WCDMA
protocol stack, the need for efficient power control algorithms in the lower layers that would
work in conjunction with the resource allocation was identified.
6
1.4 Research Questions and Limitations
The following research questions were asked to question this study.
Why choose Radio Resource Management as a research study?
The radio resource management is one of the most important engineering issues
in wireless and mobile communication systems since the radio resource spectrum is a very
limited resource. UMTS networks are only recently gaining popularity in the United States.
At this point call admission control is an important problem and will continue to do as the
number of users grow.
Why do we need more CAC schemes?
Even though many CAC schemes are present in literature and a few already de-
ployed, the conditions under which these CAC schemes were designed are continuously
changing. It is imperative to upgrade capacity planning issues and CAC schemes to meet
these ever changing conditions.
Why do we need Power Control algorithms?
CAC schemes are good for resource allocation and have no control over the other
important aspect of RRM schemes: power control and interference limitations.
Why do we need Hash Based Paging?
Paging is done at periodical intervals and depending on the number of mobiles in
a cell, this utilizes a lot of expensive bandwidth. If gain in bandwidth can be improved
by clever paging algorithms, the bandwidth gained can be used to serve users for other
applications.
The following are the research limitations in this study.
Simulation Model limitations: The management of CAC schemes involves the
WCDMA Link Budget. This includes all aspects ranging from the physical layer / air
interface, the MAC (Medium Access Control) layer, the RLC (Radio Link Control) layer
and the RRC (Radio Resource Control) algorithms in the network layer. Focus was lim-
ited to the RRC algorithms. Furthermore, the entire UMTS architecture ranging from
the UE→Node-B →RNC→CN→ Internet; play an important part in call processing. The
standard OPNETTM libraries were used for most of the nodes which proved to be compre-
7
hensive enough for the simulation needs. Only the values of their attributes were changed to
observe network performance. The RNC library was, however, changed and augmented to
improve RRC functionality. The Core Network (CN) including the Gateway GPRS Support
Node (GGSN) and the Serving GPRS Support Node (SGSN) were used to complete the
architecture. For simplicity purpose the CAC problem was based on availability of Radio
Access Bearer (RAB) at the RNC only. That is, the CAC is call based as opposed to packet
based. In addition to this, blocked calls cleared (Erlang-B) concept is used as opposed to
blocked calls queued (Erlang-C).
In the Power Control algorithm, only one WCDMA link budget parameter was
fine-tuned due to time limitations. There are many parameters that can be fine-tuned but
there will be a tradeoff between convergence time and the performance. Intuitively, the
more parameters analyzed, the better the performance but the algorithm may take a longer
time to converge. Also, the power control is developed only for the uplink because the
motivation is different: on the downlink there is no near-far problem due to the one-to-
many scenario. All the signals within one-cell originate from the one Node-B to all mobiles.
This can be compensated for by providing a marginal amount of additional power to mobile
stations at the cell edge, as they suffer from increased other-cell interference. Also, on the
downlink a method of enhancing weak signals caused by Rayleigh fading with additional
power is needed at low speeds when other error-correcting methods based on interleaving
and error-correcting codes do not yet work effectively.
Analytical Model limitations: The UMTS network is a huge network serving
many km2 of area with many hexagonal cells forming an exponential tier architecture. To
limit complexity, only a seven cell architecture was evaluated which consisted of the center
cell and six neighboring cells that comprised of the first tier neighborhood. The study of
the hierarchical tier-based structure after the first tier has been relegated to future work.
The combination of resource allocation and power control work in different layers.
The analytical computations involving both these algorithms are dealt with individually
and not as a whole.
Results limitations: Though there are many QoS parameters such as delay,
throughput, etc., that are important in analyzing the network; the focus in CAC was
8
Figure 1.1: Research Model
limited to the two main QoS parameters - Blocking and Dropping probabilities.
The rest of this dissertation is organized as follows and figure 1.1 gives us a visual
idea: Chapter 2 talks about background of UMTS and WCDMA. Chapter 3 talks about
the state of the art that exists in resource allocation and call admission control. Chapter
4 introduces the methodology and model design behind resource allocation. Chapters 5
and 6 discuss the simulation and analytical models used for call admission control. This
is the first part of the research which is shown as CAC (Call Admission Control). The
second part of the research has Chapter 7 which introduces the literature survey, model
design and methodology of the power control aspect of RRM. Chapter 8 presents the results
and discussions of RRM schemes including both call admission control and power control.
Part three is comprised of Location updates in cellular networks using bloom filters and is
introduced and its results are presented in chapter 9.
9
Chapter 2
Background
This chapter provides a basic theoretical background of the UMTS architecture
and the WCDMA air interface that is required to address the topic of this research effort. In
addition to this, a little background is given about CAC and RRM schemes and terminology
is introduced to better understand the ensuing chapters. The rest of the chapter is organized
as follows. Section 2.1 gives a brief overview of the UMTS architecture. Since the air
interface for the UMTS system is WCDMA, Section 2.2 talks about the important features
of WCDMA, its improvement over CDMA and its benefits. To better understand what
makes UMTS a heterogenous system that guarantees QoS to all traffic classes, Section 2.3
gives a description of the four bearer classes. The rationale behind CAC schemes and the
terminology in given in Sections 2.4 and 2.5.
2.1 Architecture of the UMTS system
A UMTS network consists of three interacting domains as shown in figure 2.1: User
Equipment (UE), UMTS Terrestrial Radio Access Network (UTRAN) and Core Network
(CN). The UE is a mobile that communicates with UTRAN via the air-interface. UTRAN
provides the air interface access method for the UE. CN provides switching, routing and
transit for user traffic. It also stores databases and provides network management functions.
From the specification and standardization point of view, both UE and UTRAN consist of
completely new protocols, the design of which is based on the needs of the new WCDMA
radio technology. On the contrary, the definition of CN is adopted from GSM network.
10
Figure 2.1: UMTS Network Architecure
This gives the system with new radio technology a global base of known and rugged CN
technology that accelerates and facilitates its introduction, and enables such competitive
advantages as global roaming.
User Equipment (UE): A UE consists of two parts: The Mobile Equipment
(ME) or Mobile Terminal (MT) is a radio terminal used for communicating over the Uu
interface (air-interface). The Uu interface is the air interface between the UE and the
UTRAN. The UMTS Subscriber Identity Module (USIM) is a smart-card that stores sub-
scribers identity and encryption keys, performs authentication algorithms, and supports
subscription information for the ME.
UMTS Terrestrial RadioAccess Network (UTRAN): A UTRAN consists
of two distincts elements: Node-B and Radio Network Controller (RNC). The main func-
tions of the UTRAN architecture is to: Support soft handoff and WCDMA specific radio
resource management, share and reuse voice and packet data interfaces, share and reuse
GSM infrastructure and use ATM as the main transport mechanism within UTRAN. The
interface of the UTRAN to the Circuit Switched (CS) domain of the Core Network is the
Iu-CS interface and the Iu-PS interface is the interface to the Packet Switched (PS) domain.
Node B: A Node B (logically corresponds to the GSM Base Station) converts data
11
flow between the Iub and Uu interfaces. The Iub is the Node-B to the RNC interface. Its
main duty is to perform the physical layer processing, e.g. modulation, coding, interleaving,
rate adaptation, spreading, etc.
Radio Network Controller (RNC): An RNC (logically corresponds to the
GSM Base Station Controller) controls the radio resources in its domain. RNC is the service
access point for all services UTRAN provides to the Core Network. It also terminates the
Radio Resource Control Protocol (RRC) that defines messages and procedures between UE
and UTRAN. A UTRAN may consist of one or more Radio Network Sub-Systems (RNS).
RNS is a sub-network within UTRAN that consists of one RNC and one or more Node B’s.
RNCs which belongs different RNS can be connected to each other via the Iur interface.
The Iur interface is the RNC to RNC interface. The logical function of an RNC is further
divided into controlling, serving, and drift. The controlling RNC administers the Node B for
load and congestion control. It also executes admission control and channel code allocation
for new radio links to be established by the Node B.
Serving RNC: The serving RNC is the RNC that terminates both the Iu-CS,
Iu-PS and Iub links from the core network and user equipment respectively. It performs
MAC layer processing of data to/from the radio interface. Mobility management functions
such as power control, handoff decision, etc. are also handled by the serving RNC. Note that
one UE connected to the UTRAN has one and only one SRNC. The drift RNC (DRNC)
compliments the serving RNC by providing diversity when the UE is in the state of inter-
RNC soft handoff (which requires two RNCs). During the handoff, the drift RNC does not
perform layer 2 or MAC processing; rather it routes data transparently between the Iub
and Iur interfaces.
Core Network (CN): UMTS CN is divided into Circuit Switched (CS) and
Packet Switched (PS) domains. ATM is the transport mechanism to be used in the UMTS
core. In particular, ATM AAL (ATM Adaptation Later) 2 handles circuit and packet
switched signalling while AAL5 is designed for data delivery. The core network consists of
the following elements inherited from the incumbent GSM network.
Home Location Register(HLR): An HLR is a database located in the users
home system that stores the users service profile. A service profile is created when a new
12
user subscribes to the system and remains as long as the subscription is active. It consists
of information such as user service type and roaming permission etc.
Mobile Switching Center and Vistor Location Register (MSC/VLR):
The co-located MSC/VLR serves as both the switch and database for the circuit switch
service. The MSC is used to switch the circuit switch data while the VLR function tem-
porarily hold copies of the visiting users service profile.
Gateway MSC (GMSC): It is the gateway that connects the UMTS Public
Land Mobile Network (PLMN) with the external circuit switch networks. All incoming and
outgoing circuit switch connections go through the GMSC
Serving GPRS Support Node (SGSN): SGSN has the similar functionality
as MSC/VLR except it handles packet switch connections.
Gateway GPRS Support Node (GGSN): GGSN has the same functionality
as that of GMSC except it handles the packet switch connection.
2.2 Wideband Code Division Multiple Access (WCDMA)
UMTS uses WCDMA as its air interface. This section discusses the main system
design parameters of WCDMA [5].
• WCDMA is a wideband Direct-Sequence Code Division Multiple Access (DS-CDMA)
system, i.e. user information bits are spread over a wide bandwidth by multiplying
the user data with quasi-random bits (called chips) derived from CDMA spreading
codes. In order to support very high bit rates (up to 2 Mbps), the use of a variable
spreading factor and multicode connections are supported.
• The chip rate of 3.84 Mcps used leads to a carrier bandwidth of approximately 5 MHz.
DS-CDMA systems with a bandwidth of about 1 MHz, such as IS-95, are commonly
referred to as narrowband CDMA systems. The inherently wide carrier bandwidth
of WCDMA supports high user data rates and also has certain performance benefits,
such as increased multipath diversity. Subject to his operating license, the network
13
operator can deploy multiple such 5 MHz carriers to increase capacity, possibly in the
form of hierarchical cell layers.
• WCDMA supports highly variable user data rates, in other words the concept of
obtaining Bandwidth on Demand (BoD) is well supported. Each user is allocated
frames of 10 ms duration, during which the user data rate is kept constant. However,
the data capacity among the users can change from frame to frame.
• WDCMA supports two basic modes of operation: Frequency Division Duplex (FDD)
and Time Division Duplex (TDD). In the FDD mode, separate 5 MHz carrier fre-
quencies are used for the uplink and downlink respectively, whereas in the TDD mode
only one 5 MHz is time-shared between uplink and downlink. Uplink is the connec-
tion from the mobile to the base station, and downlink is that from the base station
to the mobile. The TDD mode is based heavily on FDD mode concepts and was
added in order to leverage the basic WCDMA system also for the unpaired spectrum
allocations of the ITU for the IMT-2000 systems.
• WCDMA supports the operation of asynchronous base stations, so that unlike in
synchronous IS-95 system there is no need for a global time reference, such as a GPS.
Deployment of indoor and micro base stations is easier when no GPS signal needs to
be received.
• WCDMA employs coherent detection on uplink and downlink based on the use of
pilot symbols or common pilot. While already used on the downlink in IS-95, the use
of coherent detection on the uplink is new for public CDMA systems and will result
in an overall increase of coverage and capacity on the uplink.
• The WCDMA air interface has been crafted in such a way that advanced CDMA
receiver concepts, such as multi-user detection and smart adaptive antennas can be
deployed by the network operator as a system option to increase capacity and/or
coverage. In most second generation systems no provision has been made for such
receiver concepts and as a result they are either not applicable or can be applied only
under severe constraints with limited increase in performance.
14
• WCDMA is designed to be deployed in conjunction with GSM. Therefore, handovers
between GSM and WCDMA are supported in order to be able to leverage the GSM
coverage for the introduction of WCDMA.
2.3 UMTS QoS Bearer Classes
To support various integrated services with a certain Quality of Service (QoS)
requirement in these wireless networks, resource provisioning is a major issue [6], [7], [8].
3GPP classified bearer services of UMTS and identified them into four QoS classes, which are
mainly distinguished by their delay sensitiveness: Conversational Class, Streaming Class,
Interactive Class, Background Class. Particularly the QoS classes in UMTS are defined
through traffic parameters such as transmission rate, delay and information loss. The four
classes are described in detail below [5].
Conversational Class: The best known application of this class is speech service
over circuit-switched bearers. With internet and multimedia, a number of new applications
will require this type, for example voice over IP and video telephony. Real time conversation
is always performed between peers (or groups) of live (human) end-users. This is the only
type of the four where the required characteristics are strictly imposed by human perception.
Real time conversation is characterized by the fact that the end-to-end delay is low
and the traffic is symmetric or nearly symmetric. The maximum end-to-end delay is given by
the human perception of video and audio conversation: subjective evaluations have shown
that the end-to-end delay has to be less than 400 ms. Therefore the limit for acceptance
delay is strict, as failure to provide sufficiently low delay will result in unacceptable quality.
Streaming Class: Multimedia streaming is a technique for transferring data such
that it can be processed as a steady and continuous stream. Streaming technologies are
becoming increasingly important with the growth of the internet because most users do
not have fast enough access to download large multimedia files quickly. With streaming,
the client browser or plug-in can start displaying the data before the entire file has been
transmitted.
For streaming to work, the client side receiving the data must be able to collect
15
the data and send it as a steady stream to the application that is processing the data
and converting it to sound or pictures. Streaming applications are very asymmetric and
therefore typically withstand more delay than more symmetric conversational services. This
also means that they tolerate more jitter in transmission. Jitter can be easily smoothed out
by buffering.
Interactive Class: When the end-user, either a machine or a human is online
requesting data from remote equipment (e.g. a server), this class applies. Examples of
human interaction with the remote equipment are Web Browsing, database retrieval, and
server access. Examples of machine interaction with remote equipment are polling for
measurement records and automatic database enquiries.
Interactive traffic is the other classical data communication scheme that is broadly
characterized by the request response pattern of the end-user. At the message destination
there is an entity expecting the message (response) with a certain time. Round-trip delay
time is therefore one of the key attributes. Another characteristic is that the content of the
packets must be transparently transferred (with low bit error rate).
Background Class: Data traffic of applications such as e-mail delivery, SMS,
downloading of databases and reception of measurement records can be delivered back-
ground since such applications do not require immediate action. The delay may be seconds,
tens of seconds or even minutes. Background traffic is one of the classical data communica-
tion schemes that is broadly characterized by the fact that the destination is not expecting
the data within a certain time. It is thus more or less insensitive to delivery time. An-
other characteristic is that the content of the packets does not need to be transparently
transferred. Data to be transmitted has to be received error-free.
The main distinguishing factor between these classes is how delay-sensitive the
traffic is: the conversational class is meant for very delay-sensitive traffic, while the back-
ground class is the most delay-insensitive. The UMTS QoS classes are summarized in table
2.1 [9]. Note that the delay constraints for real time services, especially in the conversa-
tional class, with is Voice Over IP (VoIP), are very critical. Obviously there is a need for
good CAC algorithms, which will guarantee QoS and efficiently use the network’s resources.
The delay values of the other classes, represented by Web-Browsing (WWW), file transfer
16
Table 2.1: UMTS QoS Classes
Class Average Bit Rate Delay Eb/No Delay Variation Example
Conversational 12.2 150− 400ms 6.7 < 1ms VoiceStreaming 64, 144, 3842 < 10s 3.1, 3.7 < 1ms VideoInteractive 64, 144, 384 < 10s 3.1, 3.7 NA HTTPBackground NA < 10s NA NA FTP
(FTP) and streaming, are not so critical and thus there is a greater flexibility for the QoS
algorithms.
2.4 Rationale behind CAC schemes
The design of modern wireless networks is based on a cellular architecture that
allows efficient use of the available spectrum. The cellular architecture consists of a backbone
network with fixed base stations interconnected through a fixed network (usually wired) and
of mobile units that communicate with the base stations via wireless links. The geographic
area within which mobile units can communicate with a particular base station is referred
to as a cell [10]. These cells are hexagonal shaped so that there are no loopholes in coverage
area. This ensures the continuity of communications when the users move from one cell to
another.
Call Admission Control is a strategy to admit calls selectively into the system such
that network congestion and call dropping and call blocking is minimized while at the same
time guaranteeing QoS. Typical QoS parameters are blocking probabilities, transmission
rates, delay or reliability. In packet radio communications several issues, however, make this
task especially difficult to achieve: packet generation from many different sources that must
be, multiplexed within a limited set of shared resources, variable propagation characteristics
etc. [11].
The number of available channels per cell is fixed in a system whose resource is
finite and specified in time slots like Time Division Multiple Access (TDMA) or frequency
slots for Frequency Division Multiple Access (FDMA). Therefore, the CAC schemes in such
systems involve management of these hard limited channels and their fair allocation to users
17
accessing the system from within the cell and from the adjacent cell. However, UMTS is
based on Code Division Multiple Access (CDMA), which has no absolute limits on the
number of users that it can support in a cell. CDMA utilizes unit frequency reuse and thus
its resources are not upper-bounded by a hard resource limit. Therefore, CDMA based
systems are described as “soft capacity” systems. In this section we list out the reasons
for the development of good radio resource management algorithms in UMTS systems with
WDCMA as its air interface. We focus on three important factors: Interference, Power
control and Mobility.
Interference: The transmission limit in CDMA is caused by the interference gen-
erated at the base station by all the active mobile users in the same and neighboring cells
and by the propagation channel conditions in the coverage area. The increased number of
concurrent calls in a UMTS system can bring the interference level to an unacceptable level.
One of the main goals of a CAC scheme in UMTS is to limit the interference in the system.
Power Control: An important feature of CDMA mobile users is the power control,
which is not altogether accurate. The acceptance of a new connection depends on the SIR
(signal-to-interference ratio) values achievable by each existing connection once the new one
is activated. These values are functions of the emitted powers, which due to power control
mechanisms depend on the mobile user positions. Since the power available at each base
station (BS) is limited, the number of users that can be served is large if the former are
close to the BS and small if they are far away. Power control inaccuracies result in the user
terminal performing power adjustments, that may achieve a QoS (Bit Error Rate) better
or worse than the target QoS but in the same time generates excessive interference that
degrades the QoS of the other users and in the second case, the achieved QoS is lower than
that required for the user of interest and may lead to the call being in outage [4]. Ideally,
call admission control should be able to accept a call only if a new equilibrium of the power
control can be reached and to reject it otherwise.
Mobility : The other very important aspect to be considered while designing CAC
schemes in UMTS systems is that of mobility. Users’ mobility causes more complications in
wireless networks than in high-speed networks such as asynchronous transfer mode networks
[6]. An accepted call that has not been completed in the current cell may have to be handed
18
off to another cell. During the process, the call may not be able to gain a channel in the
new cell due to either limited resources or interference problems in the new cell. This leads
to call dropping which is a very important QoS parameter in UMTS systems.
2.5 Terminology used in CAC schemes
• New Call: When a mobile user wants to communicate with another user or a base
station, it must first obtain a channel or code from one of the Base Station that it
hears best. If a channel is available the user is granted that channel. This originating
call is called a new call. The user releases the channel when one of the two happens:
(1) The user completes the call and (2) The user moves to another cell.
• New Call Blocking Probability (or simply blocking probability): If all the
channels are busy, then the user is not granted the channel and is blocked. This is
called blocking probability.
• Handoff Call: The procedure of moving from one cell to another, while a call is in
progress, is called handoff. While performing handoff, the mobile unit requires that
the base station in the cell that it moves to will allocate a channel. If the channel is
allocated then it is called a Handoff Call.
• Handoff Call Dropping Probability (or simply dropping probability): When
the user is denied a channel in the cell it moves to the call is dropped and this is called
the dropping probability.
• Priority: From the user’s point of view, forced termination of an ongoing call is
clearly less desirable than blocking of a new calling attempt. It is important for a
good CAC scheme to avoid this annoying effect at the same time making sure that new
calls do not starve. Balance between the call blocking and call dropping is important
in order to provide the desired QoS requirements [12], [13], [14], [15].
• Cell Dwell Time: After a user enters a cell it is more likely to request a handoff in
the far future than in the near future, which implies that the handoff probability (is
19
a function of time elapsed after a call enters a cell [2]. After dwelling in a cell or the
same length of time, a high-speed (vehicular) user is more likely to request a handoff
than a low-speed (pedestrian) user is; which implies that the handoff probability is
also related to the speed class of the user [2].
• Uplink or Reverse Channel: The radio channel from a Mobile Terminal (MT) to
its serving Base Station (BS).
• Downlink or Forward Channel: The radio channel from the BS to the MS’s.
20
Chapter 3
State of the Art
This chapter gives the literature review of work that is related to call admission
control policies. The works of the authors listed in this chapter give a brief overview
from wireless networks of TDMA (Section 3.1) systems to the capacity planning of CDMA
systems (Section 3.2). In Section 3.3 The CAC and RRM policies related to WCDMA and
UMTS are mentioned in detail.
3.1 Before 3G and WCDMA
Papers [2], [16], [17], [6], [10], [18], [1], [8], [11], [12] talk about call admission
control policies and QoS guarantees in wireless networks such as TDMA where there is a
finite number of resources. Most of these papers have two classes of calls: new calls and
handoff calls and priority is given to handoff calls by reserving a portion of the bandwidth
for only handoff calls, popularly known as the Guard Channel Reservation Scheme.
Authors J. Hou and Y. Fang in [2] talk about the potential impact an ongoing
call may have on the resource usage of its neighboring cells. They introduce the concept
of influence curves and propose a new call bounding scheme that limits the number of new
calls being accepted in a cell. Their reasoning is that it is better to accept fewer calls than
drop ongoing calls in the future.
Authors M-H. Chiu and M. A. Bassiouni in [16] propose a predictive scheme for
handoff prioritization. This scheme works by sending reservation requests to neighboring
cells based on extrapolating the motion of mobile stations. In [17], I. C. Panoutsopoulos
21
and S. Kotsopoulos, enhance the fractional guard channel policy by allowing new calls to
be queued. They further make use of a cost function to justify their theory. Authors Y.
Fang and Y. Zhang in [6] point out that the average channel holding times for new calls and
handoff calls are significantly different and propose a two-dimensional Markov chain model
to solve the fractional guard scheme policy with queuing.
Y. Zhang and D. Liu develop an adaptive algorithm for CAC built upon the concept
of guard channels and they use an adaptive algorithm to search automatically the optimal
number of guard channels to be reserved at each base station [10]. H. Chen., S. Kumar
and C.-C. J. Kuo in [18] propose a dynamic CAC that selects the resource access threshold
according to the estimated number of incoming call requests of different QoS classes. In
[12], C. Chang, C-Ju Chang and K-R Lo analyze a hierarchical cellular system with finite
queues for new and handoff calls.
3.2 On the capacity of CDMA and WCDMA systems
In [19], A.J. Viterbi et. al. show that for terrestrial cellular telephony, the inter-
ference suppression feature of CDMA can result in a many-fold increase in capacity over
competing digital techniques. G. Karmani and K. N. Sivarajan find bounds and approxi-
mations for the capacity of mobile cellular communication networks based on CDMA. They
develop efficient analytic techniques for capacity calculations of CDMA celluar networks
[20]. In [21], a detailed description of the physical layer of ETSI WCDMA is given together
with an overview of the highe rlayers of the WCDMA air - interface. The WCDMA per-
formance based on results from the ETSI evaluation of UMTS radio-interface candidates is
presented.
Book [22] talks about the principles of spread sprectrum communications in CDMA.
22
3.3 On WCDMA and UMTS
3.3.1 Dimitriou et. al.
In [4], N. Dimitriou and R. Tafazolli present issues concerning RRM and CAC for
multimedia WCDMA systems. The aggregation of different services with different charac-
teristics like bit rate, circuit/packet switching and QoS requirements such as Bit Error Rate
(BER) and delay were analyzed. The CAC scheme was based on the maximum transmitted
power by the mobile terminal which attempts to mitigate propagation channel impairments.
They talk about how the user position within the cell affects the capacity of the
home and neighboring cells. If the user is close to home base station, then the transmitted
power will be less than the power the same user should transmit from a position near the
cell boundary. As the user gets closer to the cell boundary, the probability of reaching the
maximum allowable transmitted power increases, leading to an increased outage probability.
Also, the interference experienced by the adjacent base station can be higher and this may
lead to outage conditions for some of the existing connections in that cell.
They also discuss power control issues and how they cannot be completely accurate.
Power control inaccuracies result in the user terminal performing power adjustments, that
may achieve a QoS of BER better or worse than the the target QoS. In the first case,
the user achieves a better QoS but at that same time generates excessive interference that
degrades the QoS of the other users and in the second case, the achieved QoS is lower than
the required for the user of interest and may lead to the call being in outage.
They mention that the CAC criteria for the reverse link is based on the received
SIR at the base station. Assuming that the desired powers at the base stations by n users
within the cell are S1, S2, ...., Sn, their CAC criteria is as follows:
SIRk =Sk∑n
i=1 Si + Ioc + N≤ SIRthreshold (3.1)
where SIRk is the total received power at the base station, SIRthreshold is the desired SIR
and the base station, Ioc is the other cell interference and N is the thermal noise density.
The aggregation of three multimedia UMTS services, Speech, Video and WWW
was studied and the criteria for conducting resource allocation were analyzed. However,
23
not much information was given on capacity planning or link budgets the adaptive nature
of calls in the system. Neither was an uplink criterion given. Uplink and downlink propa-
gation parameters are uncorrelated, hence each of the bi-directional links require separate
admission criteria.
3.3.2 Capone et. al.
[23] talks about the Power Control mechanism adopted by UMTS controls the
power emitted on each channel in order to keep the SIR at the receiver at a target value.
In normal conditions, an equilibrium point is reached after some algorithm iterations and
all channels achieve the SIR target. The acceptance of a new call can create two possible
situations: the new call is safely activated since a new equilibrium can be reached, or the
new call is erroneously admitted since a new equilibrium can not be reached due to the
interference levels and the power constraints.
Ideally, call admission control should be able to accept a call only if a new equi-
librium of the power control can be reached and to reject it otherwise. This ideal behavior
can be obtained with a complete knowledge of the propagation conditions or allowing the
new call to enter the system for a trial period. More practical schemes implemented with
a distributed control must cope with a partial knowledge of the system status and may
erroneously accept or reject a call.
They calculate the received power as Pr = Ptα210
ε10
1L , where L is the path loss,
ε10 is the shadowing factor, Pt is the transmitted power. ε is the normal variate with zero
mean and σ2 variance and α2 is the gain with an exponential distribution of unit mean, due
to fast fading. The cell radius in their simulation is 300m. They calculate the path loss for
a distance of r (UE to Node-B) as:
10logL = 128.1 + 37.6logr(dB)
They adopted a traffic model where each voice user generates a single call and
arrives to the system as a Poisson process of intensity λ. The call length is exponentially
distributed as 180s. The SIR is calculated as in (3.1). Their power control model is an
iteration that is executed every 100ms and they evaluate the new power level as follows:
24
Pnew = PoldSIRtar
SIR(3.2)
This Pnew has to be less than the maximum power Pmax that can be transmitted
by the base station. Clearly this model does not talk about the heterogenous nature of
UMTS systems though it gives a CAC criteria from the base station point of view.
3.3.3 Stol et. al.
Frank Yong Li and Norvald Stol in [24] talk about a priority oriented CAC
paradigm with QoS renegotiation for multimedia services in UMTS. Their CAC criterion
is:
ηuplink ≤ ηthreshold
They use the well known capacity calculation formula under the assumption that
the background thermal noise is negligible compared to the interference level and that there
is perfect power control.
ηi =(Eb/No)i
W/Ri.υi.(1 + f) (3.3)
where ηi denotes the individual load of service i, (Eb/No)i is the bit energy to
noise ratio required for desired BER of service i, W is the UMTS chip rate of 3.84 Mcps,
Ri is the bearer bitrate of service i, υi is the activity factor of service i, f is the interference
factor from adjacent cells.
The Node-B now calculates the sum of all loads for N users, ηuplink as:
ηuplink =∑N
i=1 ηi
The ηthreshold is calculated as:
ηthreshold = 1− ξ −M.stdload + marginhandover
where ξ is a parameter controlled by uplink load control, M is a selectable param-
eter like 5, stdload is standard deviation of the changes caused by the uncontrolled calls in
the load and marginhandover reserves certain amount of capacity.
Their renegotiation is based on the fact that is a service i asking bitrate Ri cannot
be permitted into the system, the user has a choice to either refuse the connection or lower
its bit-rate and seek re-admission into the system.
25
Though the concept of QoS renegotiation was introduces here, many of the pa-
rameters used in the CAC criterion were not explained or derived analytically.
3.3.4 Victor O.K. Li et. al
Authors Zhuge and Li discuss an example of an adaptive call admission control
in [25]. This scheme is adaptive because at every instance of call admission, real time
traffic is taken into consideration with respect to the number of users and the capacity
they use in each own cell as well as their neighboring cell. Also, this scheme is different as
compared to the other schemes discussed above because it takes into consideration multi-
level service classes. In their approach, the limit on the acceptable interference level in
a cell is translated into a constraint on the number of users of each service class in the
local and neighboring cells. Most of the existing work on CDMA is on supporting non-
homogeneous mix of traffic, especially voice and data [26],[27],[28],[29],[30],[31]. The focus
is on the tradeoff between the number of voice and data users according to their blocking
or outage probability requirements [25].
The parameter to determine the acceptable interference level in a CDMA sys-
tem is the bit-energy to interference-density ratio (disregarding imperfect power control,
shadowing), calculated as:
γk =(Eb)k
Io=
Sk.W
I.Rk(k = 1......L) (3.4)
where k is the kth service class, L is the number of service classes in the cell, Sk is
the received user signal power at the base station, I is the total interference power received
at the Base Station, W is the system bandwidth, Rk is the data rate of the application,
(Eb)k = Sk/R is the bit energy in the received signal, and Io = I/W is the received
interference density.
For good communication quality for the kth service class, k must be greater than
a threshold value k*. Assuming perfect power control, k = k*, the total interference plus
noise received at the base station is approximately:
26
I =L∑
k=1
NkSk + Sout + noW (3.5)
This approximation is valid when the signal power from any signal user is small
compared to the total interference power. Nk is the number of users from the kth service
class in the local cell, Sout is the total interference power received from the neighboring cells,
and no is the power density of thermal noise. Eqn. 3.4 Also means Sk/(Rkγk) = I/W . From
eqn. 3.5
I =L∑
k=1
NkRkγkSk
Rkγk+ Sout + noW = (
L∑k=1
NkRkγk)I
W+ Sout + noW (3.6)
we now get,L∑
k=1
NkRkγk = W (1− Sout + noW
I) (3.7)
Due to dynamic range limitation on the multiple access receiver of Bandwidth
W , there exists an upper bound on the total received interference power, expressed as the
noise-density to interference-density ratio. This will guarantee system stability [32].
no
Io> η ≈ noW
I> η (3.8)
where η < 1 is dependent on the system design (η is typically chosen between 0.1
and 0.25 in the IS-95 system [29]). This is corresponding to power ratios Io/no = 6dB to
10dB [32]. Adding this constraint, eqn. 3.7 now becomes:
we now get,L∑
k=1
NkRkγk < W (1− η − Sout
I) (3.9)
Now [25] relates Sout/I to the number of users in the neighboring cells. Only the
first tier of cells are considered here. Sout is the total signal power from all users of all
service classes in the six neighboring cells received by the base station in the local cell, i.e.
we now get,
Sout =6∑
c=1
Lc∑k=1
Nic∑i=1
αkicSkc (3.10)
27
where Skc is the power of class k user received by the base station c, Nkc is the
number of class users in cell c, Lc is the number of service classes in cell c, αkic is the
path loss ratio for user i of class k in cell c. Assuming interference power is approximately
the same in each cell. The authors of [25] stress that homogeneous interference does not
necessarily mean homogeneous traffic conditions in a cell. Now we get:
Sout
I=
6∑c=1
Lc∑k=1
Nic∑i=1
αkicSkc
I=
1W
6∑c=1
Lc∑k=1
Nic∑i=1
αkicRkcγkc (3.11)
Applying eqn. 3.10 in eqn. 3.11, they obtain the constraint on the number of users
under this static model of perfect power control and no shadowing.
Lo∑k=1
NkoRkoγko +6∑
c=1
Lc∑k=1
Nic∑i=1
αkicRkcγkc < W (1− η) (3.12)
the subscript 0 indicates cell 0. Rkγk can be regarded as the fraction of system
bandwidth W ”effectively utilized” by a user of service class k. Hence the left side of eqn.
3.9 is an index of system bandwidth utilized, defined by a parameter C;
C =Lo∑
k=1
NkoRkoγko +6∑
c=1
Lc∑k=1
Nic∑i=1
αkicRkcγkc (3.13)
In addition since Rkγk is proportional to Sk/I, C is also a good estimate of the
relative interference level in the cell.
The outage probability for this system is defined as the probability of event P [C ≥
W ] < δ, where δ is dependent on the system design. Hence, it is required that:
P [C ≥ W ] < δ (3.14)
3.3.5 Schultz et. al.
The authors in [9] mention three CAC algorithms. Two of which are widely popular
and are currently deployed in UMTS systems and form the framework for this research study.
There are the Wideband Power Based and the Throughput Based CAC schemes and they
are covered in detail in the following chapter. The third scheme defined is the CAC based
on signal-to-noise-plus-interference ratio and is mentioned in detail here.
28
This admission control algorithm aims at preserving the quality of the connec-
tions measured in terms of the signal-to-noise-plus-interference ratio. They distinguish the
operation on the uplink and downlink directions.
Uplink Criterion: The total power that a given base station (BS) antenna re-
ceives is compiled by the background noise, denoted by N , and the signals from Mobile
Terminals (MT’s) connected to the considered or nearby cells, denoted by I (interference).
Let Ci and Ri be respectively the power and bit rate of the signal received from the ith
MT connected to the base station. We will assume just one active connection per MT at
the same time, although results can be easily extended to consider several connections with
different QoS requirements. The bit energy to noise plus interference density ratio is given
by:
Eb
No=
Ci/Ri
(N + I + Ci)/W=
CiPGi
N + Ii(3.15)
where W is the chip rate of the system, PGi is the so-called processing gain and
Ii = I − Ci is the interference experimented by the user i.
Upon a new MT connection request with a specific QoS demand, Node-B will
estimate the power to be received from the user to comply with the QoS error constraint,
usually given in terms of Bit Error Rate (BER) or Frame Error Rate (FER). A previous step
is to derive a target Eb/No that guarantees the error ratio required by the user. Among
others, factors such as modulation parameters, error correction techniques, geographical
location and MT movement pattern are used to map BER or FER specifications into the
target Eb/No.
Assuming M − 1 users currently connected to the BS, the requesting user is the
potential M th connected user. From eqn. 3.15, the minimum estimated required power for
the new user is:
C̃M =(Eb/No)target,M (IM + N)
PGM(3.16)
IM is the interference the new user would see if accepted, so IM + N is the whole
power that the BS is receiving. Let C̃i = 1 ≤ i ≤ M − 1, be the minimum new received
29
power predicted for the ith MT which is required to meet its target Eb/No with the effect
of the requesting terminal taken into account. Under the assumption that the requested
terminal is in the system C̃i > Ci; where Ci is the current received signal power from
the ith terminal. As the number of newly admitted mobile terminal increases, so should
the received power of the previously admitted ones, so that their required Eb/No can be
guaranteed. In turn, if C̃i = 1 ≤ i ≤ M − 1, increases, the total interference that the
requesting terminal will experience also increases; hence CM needs to be increased. The
rise of CM will further increase the required Ci value. As a result, the predicted values
need to be updated recursively based on the current interference seen by each user. After a
few iterations, the estimations will converge with a reasonable accuracy if solution indeed
exists. If the solutions diverge after a few iterations, it means that the system does not
have enough capacity to accommodate the requesting MT.
Ci =1 + N
1 + (PGi/(Eb/No)i(3.17)
an equivalent equation for the downlink is given by:
Ci =(1 + N)i
1 + (PGi/(Eb/No)i(3.18)
Now, Ci is the power with which the ith user channel is received at ithMT , and
(I + N)i is the sum of total interference (Ci included) and background noise received at
ith MT. Thus, when the M th connection is requested, the estimation of its needed received
power is:
C̃M =(1 + N)M
1 + (PGM/(Eb/No)target,M )(3.19)
Downlink Criterion: The downlink is limited by power availability rather than
by interference. In UMTS, the downlink becomes the capacity bottleneck of the system. In
the same way it was done for the uplink, error requirements are mapped into a target Eb/No
to be achieved at the MT, which we will relate this time to the power to be transmitted by
the base station. Starting from eqn. 3.15 a new equation for the received power at BS from
user i can be derived:
Ci =(I + N)i
1 + (PGi/(Eb/No)i)(3.20)
30
Where Ci is the power with which the ith user channel is received at the ith MT,
and (I+N)i is the sum of the total interference (Ci included) and background noise received
at the ith MT. Thus, when the M th connection is requested, the estimation of its needed
received power is:
C̃M =(I + N)M
1 + (PGM/(Eb/No)target,M )(3.21)
To be admitted, the interference generated in every neighboring cell must not
exceed its current resource margin. Therefore, the target cell should transmit connection
requirements to its neighboring cells and receive permission from them. Another factor
to take into account is the fact that a connection request can be a new connection but it
can also come from a handoff. The latter case should be prioritized because, in general,
interrupting a service in an active connection is more annoying to users than rejecting a
new connection. A fraction of the total resources can be reserved in a target cell for future
handoff connections coming from nearby cells. The optimum fraction should depend on the
handoff probability, which can be estimated from the traffic load in nearby cells, as well
as measurements on the position, movement direction and mobility pattern of neighboring
MT’s [9].
31
Chapter 4
Methodology and Model Design
This chapter defines the methodology used and discusses the design of the sim-
ulation model. The purpose of this research study was to investigate the radio resource
management schemes or call admission control schemes that are currently available in liter-
ature and those that are being deployed. The two most popular schemes are the Wideband
Power Based (WPB) scheme and the Throughput Based (TB) scheme. After sufficient in-
vestigation of these schemes and their performance over different network consistencies, a
new CAC scheme called Adaptive Call Admission Control (ACAC) scheme is proposed here.
Section 4.1 and Section 4.2 discuss the WPB and the TB schemes respectively. Section 4.3
presents the new ACAC scheme. The simulation model is described in detail in Section ??.
Any CAC scheme in a cellular environment involves a duplex or bi-directional link.
The uplink direction is when the mobile user is talking to the Node-B. A typical instance
maybe to seek admission for service. The downlink direction is when the Node-B is talking
to the mobile user to send beacon signals, to poll the UE or for the FCC regulated E-911
updates which are commonly called the location update procedures. It is necessary that
all the QoS parameters involved in both the uplink and downlink are satisfied every time
a new user is involved so as not to compromise the quality of the existing calls. Also, we
must keep in mind that the bi-directional links are asymmetric. Hence the following CAC
schemes have two criterions: the Uplink Criterion and the Downlink Criterion.
32
Figure 4.1: Shows Load curve and the, due to a new call, increase in Interference
4.1 Wideband Power Based Admission Control Scheme
4.1.1 Uplink Criterion
Every time a new user seeks admission into the system, it adds a certain amount of
interference to the system. The criterion for the uplink admission of the connection is based
on the comparison of the interference the new user would add to the system, if admitted,
to an interference threshold value Ithreshold. This is shown in fig. 4.1. This value should not
be exceeded by the admission of a new user. If the existing interference in the system is
Itotal, and the interference the new user would bring to the system is ∆I , then the uplink
criterion is [9], [5]:
Itotal + ∆I ≤ Ithreshold (4.1)
This ∆I can be calculated in two ways. The first case is by differentiation of the
load curve. The second case is to integrate from the old value of load factor to the new
value of load factor.
Differentiation of the load curve: This is the procedure followed in this re-
search study. Differentiation of the load curve is done in the following manner:
∆I =Itotal
(1− ηUL)∆L (4.2)
where Itotal is the total estimated interference level after admission of the new user. ηUL is
the uplink load factor in the cell serving N users and ∆L is the increase in the load factor
33
due to admission of the new user. The noise rise can be written as:
NoiseRise =Itotal
PN=
11− η
⇒ (4.3)
Itotal =PN
(1− η)⇒ (4.4)
dItotal
dη=
PN
(1− η)2(4.5)
Integration of the load curve: Integrate from the old value of the load factor
(ηold = η) to the new value of the load factor (ηnew = η + ∆L).
∆I =∫ Itotal+∆L
Itotal
dItotal (4.6)
∆I =∫ η+∆L
η
PN
(1− η)2dη (4.7)
∆I =PN
1− η −∆L− PN
1− η(4.8)
∆I =∆L
1− η −∆L.
PN
1− η(4.9)
∆I =Itotal
1− η −∆L.∆L (4.10)
where ∆L is given by:
∆L =1
1 + Wυ.Eb/No
.R(4.11)
The power increase can be considered to be the derivative of the old uplink inter-
ference power with respect to the uplink load factor, multiplied by the load factor of the
new UE, ∆L:∆I
∆L≈ dItotal
dη(4.12)
∆I =dItotal
dη∆L (4.13)
∆I =PN
(1− η)2∆L (4.14)
34
∆I ≈ Itotal
(1− η)∆L (4.15)
ηUL is given by the following equation:
ηUL = (1 + i)N∑
j=1
11 + W
(Eb/No)iRiυi
(4.16)
where
i =othercellinterference
owncellinterference(4.17)
where W is the chip rate, Rj is the bitrate of the jthuser and ∆L is given by the relation:
∆L =1
1 + Wυi(EbNo)iRi
(4.18)
R, the bitrate depends on the type of service asked. (Eb/No) is the signal energy per bit
divided by the noise spectral density and needs to meet a predetermined QoS. The noise
includes both thermal noise and interference. The activity factor of the user υi can be
considered 0.67 for speech and 1.0 for data.
4.1.2 Downlink Criterion
Considering the downlink direction, the user is admitted if the new total downlink
transmission power does not exceed a predefined target value set by the network operator:
Ptotalold + ∆Ptotal > Pthreshold (4.19)
The load increase in the downlink can be estimated on the base of the initial
power, which depends on the distance from the base station. The load increase depends
on the distance of the mobile from the base station. The minimum required transmission
power for each user is determined by the average attenuation between the base station
transmitter and mobile receiver, that is L̄, and the mobile receiver sensitivity, in the absence
of multiple access interference. Then the effect of noise rise due to interference is added to
this minimum power and the total represents the transmission power required for a user
at an average location in the cell. The total base station power can be expressed by the
following equation [5]:
BSTxP =NrfWL̄
∑Nj=1 υj
(Eb/No)j
(W/Rj)
1− ηDL(4.20)
35
where Nrf is the noise spectral density of the mobile receiver front-end. The value of Nrf
can be obtained from:
Nrf = k.T + NF (4.21)
where k is the Boltzmann constant of 1.381 ∗ 10−23 J/K, T is the temperature in Kelvin
and NF is the mobile station receiver noise figure with typical values of 5-9 dB.
4.2 Throughput Based Admission Control Scheme
4.2.1 Uplink Criterion
In the uplink, a new user is admitted only if the sum of the existing uplink load
factor ηUL and the increase in the load factor ∆L does not exceed a predetermined threshold
limit ηULthreshold[5], [9].
ηUL + ∆L ≤ ηULthreshold(4.22)
where ηUL is given by (4.16) and ∆L by (4.20).
4.2.2 Downlink Criterion
The criterion in the downlink is similar to that of the uplink [5], [9]:
ηDL + ∆L ≤ ηDLthreshold(4.23)
where ∆L is given by (12) and ηDL is given by the following equation:
ηDL =N∑
j=1
Rjυj(Eb/No)j
W[(1− αav) + iav] (4.24)
where αav is the average orthogonality of the cell. In an ideal single cell CDMA system,
downlink channels are perfectly code multiplexed, i.e., codes have a degree of orthogonality
between them. So there is no problem de/modulating while resources are available. However
in a real CDMA system, the set of codes is modulated by the multipaths channel. As a
result codes arrive at the users with a lesser degree of orthogonality. This produces downlink
interference, which is modeled as a downlink orthogonality factor.
36
4.3 Proposed Adaptive Call Admission Control Scheme
Based on certain preliminary results, we observed that the Wideband Power Based
scheme works better on a network with prevalent voice users whereas the Throughput Based
scheme works better where data users are prevalent. The most plausible explanation for
this could be because since the WPB is more power limited in the downlink, voice users
require lower power to be served than data users. Hence the downlink forms a bottleneck
for the data users in WPB. For the TB scheme the uplink is capacity limited and data
users are fewer in number at any point of time than voice users. Hence the uplink forms a
bottleneck for voice users in TB. To prove the theory that WPB works better in the case
where there are many voice users and that the TB works better when there are many data
users, the simulation was stress-tested on a wide variety of heterogeneous UMTS networks
comprising of various percentages of voice and data users. Satisfied with the results, the
need for another CAC scheme that will altogether eliminate or at the most minimize the
preferential treatment shown by both the WPB and the TB schemes was identified. This
forms the basis for the proposed ACAC.
The theory behind the Adaptive Call Admission Control [25] scheme is that it
switches between the Wideband Power Based and the Throughput Based scheme depending
on the number of each type of user present in the system at the end of a previous epoch
and the number of each type of user estimated arrival in the next epoch. Updates are done
at periodic intervals called τ . Predicting the number of users at a given time depends on
two criteria. The first one being α which is the parameter used to influence the number of
predictions in the up and coming epoch and is given by (4.25) for voice and (4.26) for data
calls.
Predictionvoice1 = α ∗ V oicen + (1− α) ∗ V̂ oicen (4.25)
Predictiondata1 = α ∗Datan + (1− α) ∗ D̂atan (4.26)
where V oicen and Datan are the number of voice and data calls that originated
in the previous epoch to the current one being predicted and similarly V̂ oicen and D̂atan
are the number of voice and data calls predicted.
37
Predictionvoice2 = β ∗ totalnumberofvoicecalls (4.27)
Predictiondata2 = β ∗ totalnumberofdatacalls (4.28)
The second criterion in (4.27) for voice and (4.28) for data influences the total
number of calls that have originated in the system since start-up. Since video and FTP
calls tend to persist in the system causing self-similarity, having β makes the prediction
better.
α and β vary between 0 and 1. The prediction is now done in the following way
for voice and data calls:
V̂ oicen+1 = Predictionvoice1 + Predictionvoice2 (4.29)
D̂atan+1 = Predictiondata1 + Predictiondata2 (4.30)
The prediction for α, β and τ are clearly very critical. They can be found either
adaptively or statistically. In this research study, adaptive (trial and error) methods through
many simulation runs are used, leaving the analytical calculations for future work.
38
Chapter 5
Simulation Modeling
Fig. 5.1 shows the simulation model. It consists of seven cells that form the UMTS
network. The radius of each cell is 1km. A Node-B serves each cell. All the seven cells
are served by the Radio Network Controller (RNC). The call admission control intelligence
lies in the RNC. Each cell has voice, video and FTP users. Voice and video users talk to
each other and the FTP users talk to the FTP server. In our simulation model we have
two types of classes: voice and data. Video and FTP users together constitute the data
class. The mobiles users have trajectories that are user-defined. The mobiles can only move
within the seven cells. Boundary conditions are strictly enforced.
5.1 Node-B Simulation Parameters
The path loss model is a urban city Okumura Hata model with a shadow fading
standard deviation of 8dB.
Shadowing: Shadowing is caused due to reflection and diffraction of the trans-
mitted signal due to terrain conditions and large objects [22]. Modeled as a Lognormal
random variable with parameters (0, σ2ε ), where σε = 8dB is a typical value . ε ∼ Ln(0, σ2
ε ).
Or equivalently the decibel value of 10log10ε has a Gaussian distribution with mean 0 and
variance σ2ε . This factor is independent of the path loss ratio. This factor is assumed
independent of each user because they vary with respect to user locations.
Path Loss Ratio (α): The exact value of the path loss ratio is unpredictable
because it is related to the user positions which cannot be determined for each user. Hence
39
Figure 5.1: Simulation Model
an approximation is used and this has been verified in simulations in [33], [34]. E[α] = 0.0474
and V ar[α] = 0.0121.
Here, we calculate the path loss ratio by the Okumura-Hata model or the Walfish-
Ikegami model. As an example we can take the Okumura-Hata propagation model for an
urban macro cell with base station antenna height of 30m, mobile antenna height of 1.5m
and carrier frequency of 1950 Mhz:
L = 137.4 + 35.2log10(R) (5.1)
where L is the path loss in dB and R is the range in km. For suburban areas, we
assume an additional area correction factor of 8 dB and obtain the path loss as:
L = 129.4 + 35.2log10(R) (5.2)
The path loss ratio is accurately calculated using the WCDMA Link Budget in Section 5.3.
40
5.2 Radio Network Controller Call Admission Control Sim-
ulation Parameters
While the basic call admission control varies from Wideband Power Based, Through-
put Based to the proposed Adaptive Call Admission Control scheme, the other CAC pa-
rameters listed below remain the same.
Uplink power control efficiency factor = 0.85. Measures the increase in
interference power due to imperfection in power control.
Uplink Loading Factor = 0.75. The loading point in the uplink that is used
as a threshold to decide whether the new user may be admitted into the system. I.e.;
the increased load that will result if a new user is admitted is compared to this threshold
to decide whether to admit or reject this user. This permits one to study the effects of
oversubscription.
Orthogonality Factor = 0.06 (type ’Pedestrian’). Orthogonality factor ranges
between 0 and 1. If 0, there is perfect orthogonality.
Downlink Other-cell Interference Factor = 1.78. Downlink Other-Cell In-
terference Factor computed at edge of cell.
Downlink Loading Factor = 0.75. The loading point in the downlink (usually
< 1.0) that is used as a threshold to decide whether the new user may be admitted into
the system. I.e.; the increased load that will result if a new user is admitted is compared
to this threshold to decide whether to admit or reject this user. Values greater than 1 are
valid. This permits one to study the effects of oversubscription.
Thermal Noise Power Spectral Density (dBm) = −174. Thermal noise
power spectral density in dBm/Hz.
5.3 WCDMA Link Budget
The WCDMA radio network dimensioning is a process through which possible
configurations and amount of network equipment are estimated, based on the operators
requirements and are related to the following [5]:
41
Table 5.1: WCDMA Link Budget of 30 kbps Voice Service
Parameter Value Variables
Transmitter (Mobile)Max Mobile Transmission Power [Watts] 0.5
As above in dBm 27.0 aMobile Antenna Gain [dBi] 0.0 b
Body Loss [dB] 3.0 cEquivalent Isotropic Radiated Power EIRP [dBm] 24.0 d = a + b− c
Receiver (Base Station)Thermal Noise Density [dBm/Hz] −174.0 e
Base Station Receiver Noise Figure [dB] 5.0 fReceiver Noise Density [dBm/Hz] 169.0 g = e + f
Receiver Noise Power [dBm] −103.2 h = g + 10log(3840000)Interference Margin 3.0 i
Total Effective Noise + Interference [dBm] −100.2 j = h + i
Processing Gain [dB] 21.0 k = 10log(3840000/30000)Required Eb/No [dB] 5.0 l
Receiver Sensitivity [dBm] −116.2 m = l − k + j
Base Station Antenna Gain [dBi] 18.0 nCable Loss in Base Station [dB] 2.0 o
Fast Fading Margin [dB] 3.0 pMax path loss [dB] 153.2 q = d−m + n− o− p
Log Normal fading margin [dB] 7.3 rSoft handover gain [dB], multicell 3.0 s
In car loss [dB] 8.0 tAllowed propagation loss for cell range [dB] 140.9 u = q − r + s− t
Coverage: Coverage Regions, area type information, propagation conditions.
Capacity: spectrum available, subscriber growth forecast, traffic density infor-
mation.
Quality of Service: area location probability or location probability, blocking
and dropping probability, end user throughput.
These dimensioning activities include the WCDMA Link Budget which covers all
possible attributes used in planning the air interface. We need to design a WDCMA Link
Budget based on our network dimensions and QoS requirements. The WCDMA Link Budget
for this research study is calculated in table 5.1. Since voice users are more prevalent, the
Link Budget is limited to voice users calculations.
Most of the parameters in the WCDMA Link Budget table have been defined
42
before. Following are the definitions for more of the parameters:
Interference Margin: It is needed in the link budget because the loading of the
cell, the load factor, affects the coverage. The more loading allowed in the system, the larger
the interference margin needed in the uplink, and the smaller the coverage area. Typical
values for the interference margin are 1.0− 3.0 dB.
Fast Fading Margin: Variations of positions of mobile users due to speed induces
a fast fading effect. Some headroom is needed in the mobile station transmission power for
maintaining adequate closed loop fast power control. This applies especially to slow-moving
pedestrain mobiles where fast power control is able to effectively compensate the fast fading.
Typical values for the fast fading are 2.0− 5.0 dB for slow moving mobiles.
Soft Handover Gain: Soft and hard handovers give a gain against slow fading
by reducing the required log-normal fading margin. This is because the slow fading is
partly uncorrelated between the base stations, and by making handover the mobile can
select a better base station. Soft handover gives an additional macro-diversity gain against
fast fading by reducing the required Eb/No relative to a single radio link. The total soft
handover gain is typically between 2.0− 3.0 dB.
Eb/No: The Eb/No requirement depends on the bit-rate, service, multi-path pro-
file, mobile speed, receiver algorithms and base station antenna structure. For low mobile
speeds, the Eb/No requirement is low but, on the other hand, a fast fading margin is re-
quired.
From the link budget shown in table 5.1, the cell range R can be readily calcu-
lated for a known propagation model, for example the Okumura-Hata model or the Walfish-
Ikegami model. For more on propagation in that environment, and it converts the maximum
allowed propagation in that environment, and it converts the maximum cell range in kilo-
metres. We can take the Okumura-Hata propagation model for an urban macro cell with
base station antenna height of 30m, mobile antenna height of 1.5m and carrier frequency of
1950 Mhz:
L = 137.4 + 35.2log10(R) (5.3)
where L is the path loss in dB and R is the range in km.
43
According to (5.1), L = u from table 5.1:
140.9 = 137.4 + 35.2log10R (5.4)
Thus, R ≈ 1 km. We choose the radius of hexagonal cells in our simulation model as 1 km.
5.4 Voice Users’ Simulation Parameters
Voice users use application of type Voice over IP (GSM quality), which produces
a load of approximately 1600 bytes/sec (13 kbps), with silence length exponentially dis-
tributed with mean 0.65 seconds and talk spurt length exponentially distributed with mean
0.352 seconds. Their Type of Service (ToS) is Interactive Voice with the highest priority.
Having a ToS helps to prioritize the calls since priority can be considered to be another
type of QoS [24]. The start time offset is the time between the end of one application to the
start of the next. This is uniformly distributed between 5 and 10 minutes. The duration of
each voice call is uniformly distributed between 3 and 5 minutes.
5.5 Video Users’ Simulation Parameters
Video users use application of type Video Conferencing (Light), at a rate of 64
kbps with the frame size in bytes being Pareto distributed with shape parameter 42.5 and
location parameter 3. The ToS is Streaming Multimedia with priority as medium. The start
time offset here is also uniformly distributed between 5 and 10 minutes and the duration of
each video call is uniformly distributed between 15 and 30 minutes.
5.6 FTP Users’ Simulation Parameters
FTP users use application of type File Transfer (Light), at a rate of 64 kbps with
the frame size in bytes being Pareto distributed with shape parameter 60 and location
parameter 1.2. The ToS is Best Effort with priority as low. The start time offset here is
also uniformly distributed between 5 and 10 minutes and the duration of each FTP call is
44
till the end of simulation time. The pareto distributions are used to provide self-similarity
in data calls.
5.7 Mobility Simulation Parameters
All mobile users move with a velocity of approximately 40 km/hr only within cells
that are defined for the network. In other words, boundary conditions are strictly enforced.
45
Chapter 6
Analytical Modeling
In this section we analyze, as an approximation, the loss probabilities of a UMTS-
WCDMA system by modeling it as a multi-rate class based system with priority. Multi-rate
systems are those that have different classes of calls with different arrival rates and different
service rates. They also have different requirements or demands from the channels/servers
based on the Type of Service. Much work has been done on multirate systems [35], [36],
[37], [38], [39]. However, the problem of assigning priorities to these classes of calls has not
been investigated. Papers [40], [41], [42], [43], provide priority in a single-rate system. A
single rate system is one where all calls have the same number of demands with different
arrival and service rates. This section marks the investigation of multi-rate systems with
priority.
The multi-rate Erlang-B loss probabilities and their derivatives for a multi-rate
system have been derived in [35] and the loss probabilities in a single-rate M/M/n/n system
with priority is given in [40].
6.1 Multi-rate Erlang-B Computation
The single rate Erlang-B model is and has been a cornerstone of numerous traffic
engineering applications that involve the calculation and optimization of blocking probabil-
ities. However, with the emerging integrated multimedia networks, one single traffic type
is often inadequate [35]. Due to this reason the authors in [35], demonstrated stable recur-
sions that have complexity of the order O(n) for both the blocking probabilities and their
46
Figure 6.1: Multi-rate Erlang-B
derivatives. This section talks about multi-rate Erlang-B computations.
The components of a multi-rate Erlang-B system are:
R - Number of classes
br - number of demands needed for each connection of class r
λr - arrival rate of a Poisson process for class r
nr - the number of active class r calls
n - number of servers in the system
1/µr - mean service time for each class r
Ar = λr/µr - offered Erlangs for class r
The classes have been defined such that b1 ≤ b2 ≤ ...... ≤ bR
The joint steady state probabilities for having a certain number of classes r cus-
tomers in the system has a product form solution [36], [37], [38], [39]:
p(n0, n1, n2, n3, ........, nR−1) =1
G(n)ΠR−1
r=0
Anrr
nr!(6.1)
where 0 ≤∑R−1
r=0 nrbr ≤ n
G(n) is the normalizing constant that will be determined such that the probabilities
add up to one.
47
∑0≤PR−1
r=0 nrbr≤n
p(n0, n1, n2, ...., nR−1) = 1 (6.2)
In [37], the function q(m) is defined as
q(m) =∑
PR−1r=0 nrbr=m
ΠR−1r=0
Anrr
nr!(6.3)
The blocking probabilities for each class r, Br is given as
Br =
∑nj=n−br+1 q(j)∑n
j=0 q(j)(6.4)
where 0 ≤ r ≤ R− 1.
In [37] and [38] show that the basic recursive formula for the q(m) function is valid
q(m) =1m
R−1∑r=0
Arbrq(m− br) (6.5)
From this approach of the recursive Erlang B formulae, the authors of [35] are
inspired to find formulae for the multi-class, multi-rate case. Their goal was to compute
numerically stable recursions for computing the B′rs. First they define a function β(m, k)
which is the probabilistic interpretation that the probability of having m-k servers busy in
a system with m servers is:
β(m, k) =q(m− k)∑m
i=0 q(i)when0 ≤ k ≤ m (6.6)
For k > 0
β(m, k) =q(m− 1− (k − 1))∑m−1
i=0 q(i) + q(m)(6.7)
The right hand side is now divided upstairs and downstairs with the sum in the
denominator. This operation together with the use of the Kaufman and Roberts recursion
leads to:
β(m, k) =β(m− 1, k − 1)
1 + 1m
∑R−1r=0 Arbrβ(m− 1, br − 1)
(6.8)
48
When k = 0, using the basic Kaufman and Roberts recursion, β(m, k) is given as:
β(m, 0) =1m
∑R−1r=0 Arbrβ(m− 1, br − 1)
1 + 1m
∑R−1r=0 Arbrβ(m− 1, br − 1)
(6.9)
The above set of recursive equations gives the probability β(m, k) for m−k servers
being busy. This computation is stable because at each step in the computation, any existing
error will be reduced. The blocking probabilities are computed from the following equation
Br =br−1∑j=0
β(n, j) (6.10)
We only need to know the values of the β(n, j) for j = 0 to bR−1. The implication
of this is that the computational complexity is of the order O(n).
6.2 Single rate prioritized system using conservation law
We have R classes in this system with R − 1 having the highest priority and
class 0 the lowest (i = R − 1, R − 2, .....0 in order of priority). We now determine the
loss probability in a prioritized UMTS-WCDMA system having multi-rate traffic. The loss
probability of highest priority class R − 1 is determined using the following Erlangs’s loss
formula (M/M/n/n) as:
pbR−1 = β(n, ρR−1) =rn
n!∑nm=0
rm
m!
(6.11)
where r = ρR−1.n and ρR−1 = λR−1/(µR−1.n).
We assume that the conservation law holds [40]. Loss conservation law states that
the expected change of a state function is zero over any finite period picked at a random
interval of a steady state. The conservation law is an approximation and simulation results
in [40] have verified that this assumption is valid when the traffic intensity is high. Let ρR−1,i
be the sum of the traffic intensity from class (R−1) through class i, i.e., ρR−1,i =∑R−1
j=i ρj .
Traffic from the lower priority classes (class i − 1 through 0) does not affect the
loss probability of the higher priority classes (class i through R− 1) due to class isolation.
For classes i = R−1, R−2, ....., 0, it is assumed that class i is completely isolated from class
(i− 1). From the conservation law we know that overall performance (i.e., loss probability
49
and throughput averaged over all classes) of the network stays the same regardless of the
number of classes and the degree of isolations Hence we can apply (6.11) to obtain pbR−1,i =
β(n, ρR−1,i). Let ci = ρi/ρ be the ratio of the traffic intensity in class i over the total traffic
intensity. We can now represent, the average loss probabilities using a weighted sum of loss
probability of each class as pbR−1,i =∑R−1
j=i cj .pbj . According to conservation law, we now
have
β(n, ρR−1,i) =R−1∑j=i
cj .pbj (6.12)
We can now obtain each pbi by starting with the highest priority class (i.e., R−1).
Since class R− 1 has the highest priority, and is completely isolated from any other classes,
pbR−1 = β(n, ρR−1) as given in (6.11).
For class R− 2, we have;
pbR−1,R−2 = β(n, ρR−1,R−2)
which is similar to:
pbR−1,R−2 =∑R−1
j=R−2 cj .pbj
according to (6.12). By equating the two, we obtain:
pbR−2 = β(n,ρR−1,R−2)−cR−1.pbR−1
cR−2
This procedure can be applied repeatedly. In general for class i, where 0 ≤ i ≤
R− 2:
pbi =β(n, ρR−1,i)−
∑R−1j=i+1 cj .pbj
ci(6.13)
6.3 Proposed Analytical Models
The previous two sections saw the implementation of multi-rate Erlang-B system
without priority and a single rate prioritized M/M/n/n system. In this section a mathemat-
ical model for a multi-rate system with priority is proposed in two parts. In the first part,
tier analysis or soft capacity modeling is not introduced. In the second part the modeling
is done keeping in mind that the WCDMA air-interface allows soft-capacity and hence the
50
Figure 6.2: Part 1: Analytical Modeling
handoff rate from the surrounding cells is considered. This is called tier analysis or soft
capacity analysis.
6.3.1 Model 1: Multi-rate Erlang-B with priority
The system model consists of three types of traffic following our simulation model:
Voice, Data and Handoff. Each have different requirements. Handoff calls have the highest
priority, then voice and data calls have the lowest priority. The WCDMA bandwidth of
3.84 Mbps is divided into n = 640 channels, each of 6 kpbs bandwidth. Voice traffic needs
30 kpbs and hence there are given 5 channels each. Data calls need 64 kpbs and hence are
given 10 channels. Handoff calls are very important and hence are given 10 channels. Data
Erlangs are constant in the system at 300 Erlangs and Handoff rates are constant in the
system at 20 Erlangs. The handoff rate is an approximation from our simulation results.
The figure depicting this system is shown in fig. 6.2.
Handoff is of class 2, voice calls are class 1 and data calls are class 0. We have
A0 = 300, A1 = 180 to 260 and A2 = 20 Erlangs and b0 = 10, b1 = 5 and b2 = 10.
51
We derive β(n, ρR−1,i) from the multi-rate system as
β(n, ρR−1,i) =
∑R−1j=i AjbjBjcj∑R−1
j=i Ajbj
(6.14)
The cj ’s are introduced here to balance the traffic intensity which is a part of the conserva-
tion law into the multi-rate Erlang-B analysis.
Since we are inheriting probabilities from the multi-rate system where there are
multiple demands into a single rate M/M/n/n system, we weight the pb′si with a fudge
factor: bi/∑R−1
i=0 bi. As in (6.14), this fudge factor is introduced into the conservation loss
probabilities due to the reason that now the conservation loss is serving as an approximation
for not a single rate system but in fact the multi-rate system. The loss probabilities for
classes R− 2 to 0 can be now determined in the following manner:
pbi =
PR−1j=i AjbjcjBjPR−1
j=i Ajbj−
∑R−1j=i+1 cj
bjPR−1
j=i+1 bjpbj
ci(6.15)
where R− 1 < i < 0.
6.3.2 Model 2: Multi-rate Erlang-B with priority and tier analysis
As shown in figure 6.3, the cellular network is comprised of the center cell and
6 neighboring cells surrounding it referred to as the first-tier neighborhood. In model 1,
the handoff rate is given as a constant. Here, we adapt the handoff rate depending on the
number of voice and data calls that exist in the first-tier neighborhood. We concentrate on
the number of handoffs from the first-tier neighborhood to the center cell.
It is assumed that the mobile stations move randomly and spread all over a cellular
coverage area. The first-tier neighborhood has six times the number of calls that exist in
the center cell. Depending on the number of active voice and data calls in the first-tier
neighborhood, we can calculate the number of channels used or the number of active calls
of each class in the first-tier neighborhood.
Channels used by class data(0) calls in center cell, ch0 = (1−B0)b0A0
Channels used by class voice(1) calls in center cell, ch1 = (1−B1)b1A1
52
Figure 6.3: Part 2: Analytical Modeling with tiers
where, B0 and B1 are the blocking probabilities from the multi-rate Erlang-B
model. A0 and A1 are the traffic Erlangs of data and voice calls. b0 and b1 are the demands
of each class in the multi-rate Erlang-B system.
Number of active calls in the center cell ∗6 = Number of active calls in the first-tier
neighborhood
Number of active calls in the center cell, m = ch0 + ch1
Number of active calls in the first-tier neighborhood, n = 6 ∗ (ch0 + ch1)
Handoff rate from the first-tier neighborhood into center cell= 16 ∗ n ∗ µ2
Now that we know the handoff rate statistically, we can apply it to multi-rate
system with conservation law.
53
Chapter 7
Power Control
7.1 Introduction
In the previous chapters we have seen the importance of resource allocation, the
state of the art that exists in the field, the extensions and improvements to these presented
in this research. Previous chapters have been devoted to resource allocation from a traffic
point of view. The input to the system was the Erlangs that each class provides to the
system and the output was observed as the two main QoS parameters; the Blocking and
Dropping Probabilities. The work was mainly concentrated in the upper layers. Mainly,
is used here, because though the air interface parameters were used for calculations for
resource control in the upper layers, none of the parameters, were ever tweaked, fine-tuned,
adapted or studied to see how they can perform in conjunction with resource allocation to
improve Radio Resource Management. This chapter concentrates on Power Control.
Broadly Radio Resource Management in UMTS-WCDMA systems can be
classified in a two-fold manner:
1. Resource Allocation :Efficient Call Admission Control algorithms in the upper layers
2. Power Control : Efficient Power Control algorithms in the lower layers
A good RRM scheme takes into consideration power control and resource alloca-
tion. Resource allocation resides in the higher layers and deals with the problem of call
admission after ensuring that adequate requirements of Signal to Interference (SIR) ratios
are met by the power control algorithms. Between these two main categories, a multitude
54
of algorithms like Admission Control, Power Control, Handover Control, Load Control and
other packet scheduling functionalities [5] are covered.
Power Control is important because a UMTS system has heterogeneous traffic
scenarios with different transmission rates and different QoS requirements. The acceptance
of a new connection depends on the SIR (signal-to-interference ratio) values achievable
by each existing connection once the new one is activated. These values are functions
of the emitted powers, which due to power control mechanisms depend on the mobile
user positions. Power control inaccuracies result in the user terminal performing power
adjustments, that may achieve a QoS better or worse than the target QoS but in the same
time generates excessive interference that degrades the QoS of the other users and in the
second case, the achieved QoS is lower than that required for the user of interest and may
lead to the call being in outage [4]. An indication of a QoS requirement is the Energy per Bit
to Noise Ratio given by Eb/No and often times the SIR and Eb/No are used interchangeably.
Ideally, call admission control should be able to accept a call only if a new equilibrium of
the power control can be reached and to reject it otherwise.
7.2 Problem Description
In real-time CDMA systems, all the active wireless stations transmit simultane-
ously to the Node-B that they are assigned to in the uplink (reverse link). Every wireless
user introduces noise in the form interference to every other wireless station’s uplink com-
munications within the cell. The higher a wireless station’s transmitting power, the better
throughput that user gets for its connection, but the higher interference it causes for the
other wireless stations in the meantime. Therefore there is a dynamic trade-off between
each individual user’s throughput and the total throughput of the system [44].
In figure 7.1, mobile Stations A and B operate within the same frequency, separable
at the Node-B only by their respectable spreading codes. It may happen that B at the cell
edge suffers a path loss, say of 70dB above that of A which is near the Node-B. If there
were no mechanism for A and B to be power-controlled to the same level at the Node-B, A
could easily overshout B and thus block a large part of the cell, giving rise to the so-called
55
Figure 7.1: Near Far Effect in WCDMA
near-far problem of CDMA. The optimum strategy in the sense of maximizing capacity is
to equalize the received powers RA and RB of all the mobile users at all times.
The main power control schemes in WCDMA are open-loop, fast closed-loop and
outer loop power control. These are used to solve the problem of near-far effect of CDMA
[5].
While one can conceive open-loop power control mechanisms that attempt to make
a rough estimate of path loss by means of a downlink beacon signal, such a method would be
far too inaccurate. The prime reason for this is that the fast fading is essentially uncorrelated
between uplink and downlink, due to the large frequency separation of uplink and downlink
band of the WCDMA FDD mode. Open-loop power control is used to provide a coarse
initial setting of the UE at the beginning of a connection.
The solution to power control in WCDMA is fast closed-loop power control. In
closed-loop power control, the Node-B performs frequent estimates of SNR and compares
it to a target SNR in the uplink. If the measured SNR is higher than the target SNR, the
Node-B commands the UE to lower the power; if it is too low it will command the UE to
56
increase its power. This measure-command-react cycle is executed at the rate of 1500 times
per second (1.5kHz) for each UE and thus operates faster than any significant change in
path loss and this is even faster than the speed of fast Rayleigh fading for low to moderate
UE speeds. Thus closed-loop power control will prevent any power imbalance among all the
uplink signals received at the Node-B.
The same closed-loop power control is also used on the downlink, though here the
motivation is different: on the downlink there is no near-far problem due to the one-to-many
scenario. All the signals within one-cell originate from the one Node-B to all mobiles. It
is, however, desirable to provide a marginal amount of additional power to mobile stations
at the cell edge, as they suffer from increased other-cell interference. Also, on the downlink
a method of enhancing weak signals caused by Rayleigh fading with additional power is
needed at low speeds when other error-correcting methods based on interleaving and error-
correcting codes do not yet work effectively.
Closed-loop power control works on a fading channel at low speed. Closed-loop
power control commands the mobile station to use a transmit power proportional to the
inverse of the received power (or SIR). Provided the mobile station has enough headroom
to ramp the power up, only very little residual fading is left and the channel becomes an
essentially non-fading channel as seen from the base station receiver.
While this fading removal is highly desirable from the receiver point of view, it
comes at the expense of increased average transmit power at the transmitting end. This
means that a mobile station in a deep fade using large transmission power, will cause
increased interference to other cells.
Outer-loop power control adjusts the target SIR according to the needs of the
individual radio link and aims at maintaining a constant quality of parameters. Usually
these are Bit Error Rate (BER), Block Error Rate (BLER) or Eb/No. This is useful in that
the required SIR depends on the UE speed and its multi-path profile which are different for
different UE’s. Setting the target SIR for the worst case would lead to wastage of capacity.
Thus the best strategy is to let the target SIR float around the minimum value that just
fulfills the required target quality. The target SIR set-point, as shown in figure 7.2, will
change over time as the speed and propagation characteristics change. Outer loop power
57
Figure 7.2: Outer Loop Power Control in WCDMA
control is implemented by having the Node-B tag each uplink user data frame with a frame
reliability indicator. This Power Control resides in the RNC because this function should
be performed after a possible soft handover combining[5].
7.3 Previous Work
Previous work in [45], [46] and [47] was focussed on Radio Resource Control (RRC)
in a UMTS-WCDMA system. This work identified the need for power control schemes to
work in conjunction with resource allocation for efficient RRM.
Typically power control can be broken down as ”Centralized or Distributed” with
the air interface being ”CDMA or WDCMA”. Some research works on all the mobiles
having ”imperfect and unequal” received powers and some on ”perfect and equal”. Some
papers assume that the mobiles can move within its own cell and others give mobility its
deserved importance. Grades of service are also important to consider. While some assume
that all the users have the same grade or quality of service, others are more realistic in
assuming heterogenous quality of users.
Many papers have introduced several different concepts for adaptive or dynamic
58
changes of different QoS parameters [25], [48], [49], [50], [51], [52]. The focus of most of
these papers were to obtain equal received powers in the uplink. This is not realistic since
in the uplink each UE is subject to varying multi-path propagation characteristics and their
distance from the Node-B varies.
Some of the papers developed algorithms where there is a centralized approach
(example RNC) [?], [?]. This requires the central controller to have all the knowledge
about the signal strengths of all the active radio links. While the efficiency is good, a
centralized control adds infrastructure, latency and increases the network complexity and
are more complicated. This is mainly due to the required detailed knowledge of radio channel
centrally which is not readily available in real time as far as multi-cell mobile networks are
concerned [50]. Distributed control is described in [?]. Some of these papers also assume
that once a UE is admitted in a cell, its stays so for the entire call duration. Mobility is
not given importance.
This research is different in that, it starts with adaptively fine-tuning the QoS
parameters by using Monte Carlo simulations and extending it to adapt the final power
required by each UE to transmit in a fashion that adheres to resource management rules.
‘Adaptive Uplink Power Control (AUPC)’ is introduced in this paper. The results of Adap-
tive Uplink Power Control (AUPC) are compared with the Outer Loop Power Control
(OLPC) and they show that this algorithm possesses fast convergence properties, ensures
limited interference in the system and provides efficient utilization of the WCDMA spec-
trum.
The rest of this chapter is organized as follows: Section 7.4 takes a power con-
trol parameter and dynamically updates the step size by using Monte Carlo Simulations.
It discusses the way both OLPC and AUPC perform under the given simulation model.
Section 7.5 gives the calculations required to analytically predict the spectral efficiency of
a WCDMA cell and 7.6 using the results obtained to adapt the transmission power of the
UE. The simulation model is given is 7.7. Results and conclusions follow.
59
7.4 Step Size Evaluation of Eb/No
7.4.1 In Outer Loop Power Control
Different QoS requirements can be used to perform calculations in the OLPC.
They can be the Bit Error Rate (BER), Block Error Rate (BLER), Signal to Noise Ratio
(SNR) or the Energy per bit to Noise ratio (Eb/No). There is a unique mapping from
the BER requirement of an uplink connection to the required Eb/No value at the Node-
B. This mapping depends on factors such as modulation scheme, interleaving method and
error-correction scheme [44]. Keeping this in mind, Eb/No is used as the parameter under
consideration. OLPC adjusts the target Eb/No according to the needs of the individual
radio link and aims at maintaining a constant quality of parameters. In this section we
analyze by how much this Eb/No target needs to be adjusted which, due to the UE speed,
changes the multi-path propagation environment. This is useful in that the required SIR
depends on the individual UE speed because of which its multi-path profile are different
for different UE’s. Setting the target Eb/No for the worst case would lead to wastage of
capacity [5]. The algorithm for the OLPC is as follows:
1. Start with an initial random value of (Eb/No)j for each user and a target value of
(Eb/No)target
2. Compare Eb/No and (Eb/No)target
3. While ((Eb/No)j 6= (Eb/No)target)
If (Eb/No)j ≤ (Eb/No)target;
Eb/No = Eb/No + 1.0
Else If Eb/No > (Eb/No)target;
Eb/No = Eb/No − 1.0
The step-size here is value of 1.0 in dB. Clearly, there are many disadvantages
to this scheme. Firstly, this algorithm assumes a constant step size of 1.0 dB. UE’s are
widely spread across the cell, each having its own multi-path propagation characteristics
60
and different mobile velocities, leading differences in time-varying characteristics of their
Eb/No values. Secondly, we know that the according to WCDMA Link Budget calculations
the average Eb/No requirements for voice users are from 3 to 5 dB and for data from 1 to 3
dB[5]. Having a step size of 1.0 dB (between 20 and 100 percent) will lead to convergence
issues and thus to instability in the system. In the following section, the paper describes
the actions it takes to combat these issues.
7.4.2 In Adaptive Uplink Power Control
This section introduces an algorithm to fine-tune the step size change in individual
Eb/No values. Papers [50], [52] show different variations of dynamically changing the step-
size. In this paper, the step-size is dynamically changed keeping in mind the effects of the
interference caused by the other users in the cell and using a linear prediction algorithm
that considers the averaging effects of the other users. We first give the algorithm required
to adaptively update the step-size for tuning the Eb/No parameter.
1. Start with an initial random value of Eb/No and a target value of Eb/No
2. Set stepT imesUPj and stepT imesDNj to 0 for all users
3. Compare (Eb/No)j and (Eb/No)target
4. While ((Eb/No)j 6= (Eb/No)target)
If (Eb/No)j ≤ (Eb/No)target;
stepT imesUPj = stepT imesUPj + 1;
(Eb/No)j = (Eb/No)j + (α ∗ stepT imesUPj/N)
Else If Eb/No > (Eb/No)target;
stepT imesDNj = stepT imesDNj − 1;
(Eb/No)j = (Eb/No)j − (α ∗ stepT imesDNj/N)
where stepT imesUPj and stepT imesDNj is the count of how many times (Eb/No)j
has been increased or decreased for a particular UE. α is our linear prediction adaptive
61
parameter. This can be found heuristically or statistically. Here it has a value of 1.0 and is
found adaptively. Analytical calculations of α has been left for future work. N is the total
number of users in the system. Initial simulation results showed that averaging the values
over N , fine-tuned the step-size further. The probable explanation for this is the fact that
mobiles in each others vicinity, may have the same multi-path propagation characteristics.
The following section discusses how the (Eb/No)j for each UE calculated is used to calculate
the powers need by each UE to transmit and eventually to discuss the effects of this on the
spectral efficiency of the WCDMA cell. This is understandably because the final aim for
power control is to minimize the power required for the UE so as to just meet the criteria for
transmission. Doing this, will ensure less interference and since WCDMA is an interference
limited system, this will lead to decrease in the outage probability. Here outage is defined
as the condition where the interference has reached an undesired limit and the system is
unstable.
7.5 Spectral Efficiency of a WCDMA cell
The theoretical spectral efficiency of a WCDMA cell can be calculated as shown
below. Eb/No, is defined as the ratio of the energy per user bit to the noise spectral density:
(Eb/No)j = PGj ∗Signalofuserj
(Totalreceivedpower −OwnPower)(7.1)
where PGj is the processing gain of user j.
PGj =W
Rj(7.2)
(Eb/No)j =W
νjRj∗ Pj
Itotal − Pj(7.3)
where W = 3.84 ∗ 106 is the WCDMA chip rate, Pj is the received signal power from user j
and Itotal is the total received wide-band power including thermal noise power in the base
station. Pj thus becomes:
Pj =1
1 + W(Eb/No)j .Rj .νj
∗ Itotal (7.4)
62
(Eb/No)j is calculated from section 7.4, Rj is the Bit Rate of user j, which is taken as
32kbps for voice users and 64kbps for data users. νj is the activity factor of user j which is
taken as 0.67 for voice users and 1.0 for data users.
We know that Pj = Lj ∗ Itotal, where:
Lj =1
1 + W(Eb/No)j .Rj .νj
(7.5)
The total received interference, excluding the thermal noise PN , can be written as
the sum of the received powers from all N users in the same cell
Itotal − PN =N∑
j=1
Pj =N∑
j=1
Lj .Itotal (7.6)
The load factor, ηUL, is defined as the total load the number of users in the system
is offering the system and is given by:
ηUL =N∑
j=1
Lj (7.7)
where, N is the total number of users in a cell. We must also take into consid-
eration the interference from the other cells which is the ratio os the other cell to own
cell interference, i = 0.55 (assuming macro cell with omnidirectional antennas). ηUL now
becomes:
ηUL = (i + 1)N∑
j=1
Lj = (i + 1)N∑
j=1
11 + W
(Eb/No)j .Rj .νj
(7.8)
Eqn. 7.9 is called the load equation and can be used as a semi-analytical prediction
of the average capacity of a WCDMA cell.
We know that, Itotal − PN = ηUL ∗ Itotal, Itotal now becomes:
Itotal =PN
1− ηUL(7.9)
where PN is the thermal noise power which is −174 dBm. From equation 7.4, we can
calculate the individual powers needed by the UE to transmit.
63
7.6 Adaptive Calculation of Pj
Once we know the Eb/No and Lj , we can calculate the new power as the product
of the interference and Lj .
Pj = Lj ∗ Itotal (7.10)
Under ideal circumstances of an infinite capacity, this would be the power that a
UE will need to transmit in the uplink and will be granted the capacity it needs without
causing significant interference in the system or compromising the QoS of the other UE’s
in the vicinity. But, this is not the case mostly. This power may exceed the limitations set
by the WCDMA link budget and the UE may be refused admission. For this reason, this
paper introduces a predicted value of the power to adapt in its next cycle.
Let Pj be the power required for the UE to transmit. The maximum power that
the UE can transmit is taken as Pmax = 0.5 Watts. If Pj exceeds the maximum power a
UE is able to transmit, the following adjustments are made:
P̂j = Pj + 10−6 (7.11)
When the requested power makes the total power exceed the limit, we still grant
the UE 1 micro Watts [51]. We now need to adjust the target Eb/No since the requested
transmission power was not granted.
(Eb/No)j = 10.0 ∗ log10(10.0(Eb/No)j
10.0 ) ∗ P̂j (7.12)
We also need to make sure that the we don’t lower the target (Eb/No)j more than
1.0. If (Eb/No)j is less than 1.0, then make (Eb/No)j = 1.0.
Alternatively, (Eb/No)j could have been recalculated by feeding the values into
7.4.2 and recalculating the P̂j . The stability of the model with the recursive procedure was
suspected and this is out of the scope of this paper.
64
7.7 Simulation Model
In our simulation model we have one WCDMA cell with a total of N = 300 mobile
users. The first 100 are voice users with a bit rate of 32 kbps and a voice activity factor
of 0.67. The remaining 200 users are video and FTP users, collectively called data users.
They have a bit rate of 64 kbps and a voice activity factor of 1.0. The target Eb/No values
for voice and data are respectively 5.0 and 3.0 dB. This is according to specifications in
[51] and the WCDMA Link Budget [5].The initial values of Eb/No are taken from a random
distribution between 1 and 8 dB. the reasoning being that having a wide range values show
the effect of convergence better. The following assumptions are further made and dedicated
to future work: Handover Control is not taken into consideration and Power Control is only
considered in the Uplink. All simulations are carried out in MATLAB v6.5.1.
7.8 Summary
In most of the world third generation UMTS with WCDMA as its radio access
interface is already a reality. Using WCDMA as the air interface as its advantages and
disadvantages. The advantages being extended coverage and higher capacity and ability to
support previous generation systems. The disadvantages being the expensive radio spectrum
in itself. To make efficient use of the radio spectrum, many radio resource management
schemes need to be implemented.
This chapter introduced the concept of fine-tuning certain Power Control param-
eters and then adaptively choosing the transmit power of the UE to increase the spectral
efficiency of the WCDMA system, which is an expensive air interface. The advantage of
such a scheme is the simplicity of fine-tuning and Monte Carlo simulations. Adapting other
parameters other than the one chosen is an interesting topic if research. Further, explo-
ration of forming a closed-loop feedback system between fine-tuning the QoS parameter
under consideration and adapting the UE power to observe the effects of convergence and
stability is an interesting exploratory avenue.
65
Chapter 8
Results and Discussions
This chapter presents the results collected in this research study. The results are
broadly classified into three categories:
• Call Admission Control
• Power Control
• Hash Based Paging
Section 8.1 presents results from the Call Admission Control part of this research.
These include comparison of the Call Blocking and Dropping of the three CAC schemes
studied extensively: Wideband Power Based, Throughput Based, Adaptive Call Admis-
sion Control. These results are compared with the multi-rate, prioritized analytical model
and then with the tier-based analytical model. Results from mobility control are then
compared. At each stage of the results in this section, it will be observed that analyzing
the call admission control with respect to various aspects is a progressive improvement in
performance.
Section 8.2 presents results from the second part of this research study, i.e. Power
Control. The results presented will show how fine-tuning the WCDMA link budget at-
tributes; improves performance by observing various parameters. Eventually we will see
how the combination of call admission control and power control will improve performance
of the Call Blocking and Call Dropping Probabilities.
66
8.1 Call Admission Control
The network paramters are ηUL = 0.75, the maximum base station power = 37dB.
The rest of the parameters are the same as the WCDMA Link Budget presented in Chapter
4.
The ACAC parameters are: α = 0.3, β = 0.9 and τ = 100seconds. These were set
adaptively by many trial and error simulation runs.
In order to study the three schemes it was imperative to have an heterogenous
UMTS system. Hence the following user parameters were set.
Voice users run an application called Voice Over IP GSM quality. The silence
length and the talk spurt length are exponentially distributed with means 0.65 and 0.354
seconds respectively. The ToS was set to Interactive Voice which has the highest priority.
The voice users start simultaneously and uniformly between 100 and 110 seconds after
simulation start time. Each user runs for a duration that is uniformly distributed between
3 and 5 minutes and their inter-repetition time is serial and is exponentially distributed
with a mean of 300 seconds until end of simulation.
Video users use application of type Video Conferencing (Light), at a rate of 64
kbps with the frame size in bytes being Pareto distributed with shape parameter 42.5 and
location parameter 3. The ToS is Streaming Multimedia with priority as medium. The
start time offset here is also uniformly distributed between 100 and 110 seconds and the
duration of each video call is uniformly distributed between 15 and 30 minutes with an
inter-repetition time of 300 seconds until end of simulation.
FTP users use application of type File Tranfer (Light), at a rate of 64 kbps with
the frame size in bytes being Pareto distributed with shape parameter 60 and location
parameter 1.2. The ToS is Best Effort with priority as low. The start time offset here is
also uniformly distributed between 100 and 110 seconds and the duration of each FTP call
is till the end of simulation time. The pareto distributions are used to provide self-similarity
in data calls.
67
8.1.1 Single Run Scenario
The single run was conducted to study and compare how the Wideband Power
Based, Throughput Based and Adaptive Call Admission Control schemes perform under
similar network conditions. This study forms the basis of future results.
Fig.8.1 compares the Data, Voice and Total Blocking Probabilities (pbdata, pbvoice
and pbtotal) across the three schemes: the Wideband Power Based, Throughput Based and
Adaptive Call Admission Control. pbvoice indicates the blocking probability of the voice calls
only. pbdata is the blocking probability of video and FTP calls combined. pbtotal indicates
the blocking for both voice and data calls combined.
The x-axis shows the simulation run time which is 10, 000 seconds and the y-axis
shows the percentage of blocking probabilities. The time between 100 and 1500 seconds
is designated as the warm-up period since all users start uniformly between 100 and 110
seconds which increases the bandwidth demand on the system and hence the blocking
probabilities. After the system has reached steady state, we observe that the Throughput
Based scheme works better than the Wideband Power Based scheme in reducing pbdata and
the Wideband Power Based works better than Throughput Based in minimizing the pbvoice.
The ACAC scheme proposed here minimizes the preferential treatment and both the pbdata
and pbvoice and hence the overall pbtotal.
Wideband Power Based and Throughput Based give preferential treatment de-
pending on the Type of Service. We observe that the ACAC minimizes this preference
and hence, we deduce from these results that in a heterogenous UMTS system the ACAC
works best. In order to validate the results in this section, confidence intervals (C.I.) for To-
tal Blocking Probability (pbtotal) and Total Dropping Probability (pbdropping)were collected
which are presented in the following section.
8.1.2 Confidence Intervals
A single run scenario though effective in pointing out the premise of a problem
is not very efficient in validation. In order to confirm the basis of the premise, confidence
intervals need to be calculated for a measure of treatment effect that shows a range within
68
Figure 8.1: Comparison of Data, Voice and Total Blocking Probabilities of 3 schemes
69
Table 8.1: Confidence Interval for Total Blocking Probability
Scheme Total Blocking Probability
Wideband Power 37.5± 7.5Throughput 51.2± 12.5
ACAC 3.1± 0.05
Table 8.2: Confidence Interval for Total Dropping Probability
Scheme Total Dropping Probability
Wideband Power 13.1± 2.3Throughput 15.65± 3.25
ACAC 5.2± 0.08
which the true treatment effect is likely to lie. This section presents the confidence intervals
of the comparison of the two schemes.
Figures 8.2 and 8.3 show the C.I. for pbtotal and pbdropping for the Throughput
Based, Wideband Power Based and ACAC schemes respectively. These C.I. are obtained
from 80 runs of 10, 000 seconds each. The y-axes show the percentage of blocking and
dropping plotted against uplink loading factor (ηUL) values 0.7, 0.75 and 0.8 in the x-axes.
We observe that the ACAC has the lowest values. In addition to this we also observe that
the C.I. for the pbtotal and pbdropping values of the ACAC are statistically different from the
Wideband Power Based and the Throughput Based. i.e., neither do the intervals overlap
with either the Wideband Power Based values or the Throughput Based values nor do they
include 0. This ’statistical difference’ in the analysis is important to any determination of
confidence intervals. The results in figures 8.2 and 8.3 are documented in tables 8.1 and 8.2
for easier reading. In this section, we conclude that ACAC works best in a heterogeneous
environment by minimizing the preferential treatment that is shown by both the Wideband
Power Based and the Throughput Based schemes.
70
Figure 8.2: Comparison of Data, Voice and Total Blocking Probabilities of 3 schemes
Figure 8.3: Comparison of Data, Voice and Total Dropping Probabilities of 3 schemes
71
Figure 8.4: Comparison of Analytical and Simulation Results
8.1.3 Comparison of analytical and simulation results
In this section we compare the simulation results of the ACAC scheme with our
multi-rate, prioritized model. Handoff, voice and data are the three classes of service with
handoff having the highest priority and data the lowest.
The WCDMA bandwidth of 3.84 Mcps is broken down to 640 channels of band-
width 6kbps each. Each voice and data users require 5 and 10 channels respectively. A
uniform distribution is used to differentiate between voice handoff calls and data handoff
calls. This can be used in future work when the handoff calls are further divided into two
classes: the voice handoffs and the data handoffs. The user parameters while remaining
the same as the previous two sections, the rate at which the calls are generated changes by
giving fixed values to each class. Data and handoff provide constant traffic to the system
at 300 and 20 Erlangs respectively and voice traffic is varied from 260 to 180 Erlangs as
shown in fig. 8.4. The x-axis shows the variation in voice Erlangs and the y-axis shows
72
the percentage of blocking and dropping. This figure compares the blocking probabilities
of data and voice and handoff calls with their C.I. means obtained from simulation.
C.I. are obtained for the ACAC scheme from 80 runs of 10, 000 seconds each. The
C.I. do not overlap with each other or include zero, thus giving us statistical difference. The
simulation and analytical results follow the same trend. Data blocking has the largest value
because the data class has the lowest priority. We observe that for the handoff class, both the
analytical values and the C.I. mean are zero. This is because handoff class has the highest
priority that is treated as a M/M/n/n system and it’s Blocking Probability is determined
using the Erlang loss formula. For higher voice Erlangs, the analytical values of both data
and voice blocking probability, lie well within their respective C.I., and the difference in
their values are smaller and more accurate. This happens because the conservation law
approximation works better at high traffic intensities. The simulation is modeled with a
number of attributes and the soft capacity of the WCDMA. Hence the results have a lower
value than the analytical model which does not include soft capacity modeling.
8.1.4 Comparison of Simulation and Analytical Results with Tier Anal-
ysis
Figures 8.5 and 8.6 compare the confidence interval means, the analytical results
from section 6.3.1 and the analytical results from section 6.3.2 of Data Blocking and Voice
Blocking respectively. Section 6.3.1 deals with the multi-rate system with conservation law
without tier analysis where as section 6.3.2 deals with tier analysis.
We observe from the figure that the results from section 6.3.2 are closer and more
accurate with the confidence interval means from the simulation as compared with section
6.3.1. This is due to the fact that the handoff rate is not a constant but it is calculated
adaptively depending on the number of voice and data calls in the neighboring cells. Also
observed, but not shown here, the Handoff or Dropping Probabilities of the C.I. mean and
the analytical results are zero since they have the highest priority in the M/M/n/n system.
73
Figure 8.5: Comparison of Data Blocking with and without Tier Analysis
Figure 8.6: Comparison of Voice Blocking with and without Tier Analysis
74
Figure 8.7: OLPC and AUPC with respect to Average (Eb/No)j
8.2 Power Control
The important results that are collected and observed are (Eb/No)j , Lj , ηUL, Pj
and NoiseRise. The implications of these results as we shall see, lead to a semi-analytical
predication model for the following factors in a WCDMA system: Interference, Noise Mar-
gin, Pole Capacity, Spectral Efficiency and Load Factors.
8.2.1 Comparison of OLPC and AUPC with respect to Average (Eb/No)j
This section compares the average values of (Eb/No)j , which is the average of
(Eb/No)j values of users in the system; (∑N
j=1(Eb/No)j)/N . Figure 8.7 tells us that the
AUPC scheme is not only more stable but converges more gradually to a lower value. The
x-axis is the number of Monte Carlo simulations and the y-axis shows the average values of
(Eb/No)j .
75
Figure 8.8: OLPC and AUPC with respect to Total ηUL
8.2.2 Comparison of OLPC and AUPC with respect to Total ηUL
This section compares the average values of the total ηUL that exists in the system;∑Nj=1 Lj of the OLPC and AUPC. As in the case of average (Eb/No)j , the total ηUL is more
stable and converges to a lower minimum value. The implications of these results is that
the AUPC keeps the noise to a more minimum value than the OLPC. By keeping this to a
minimum, it reduces the total ηUL offered to the system. This is the indication of the pole
capacity in the system. The pole capacity is directly related to the Noise Rise as shown in
the following section.
8.2.3 Comparison of OLPC and AUPC with respect to Noise Rise
Since NoiseRise = 11−ηUL
, as ηUL → 1, NoiseRise → ∞. Thus minimizing ηUL
prevents the pole capacity from reaching ∞. Noise Rise is a good indication of when the
system reaches pole capacity; i.e., the interference has reached its maximum and if the
76
Figure 8.9: OLPC and AUPC with respect to Noise Rise
system is in outage. From figure 8.9, we observe that the AUPC works better keeping the
Noise Rise to a minimum value than the OLPC. The y-axis shows the Noise Rise in the
system in dB and the x-axis shows the number of Monte Carlo iterations.
8.2.4 Comparison of OLPC and AUPC with respect to (Eb/No)j
Figures 8.10 compares the individual values of (Eb/No)j each mobile. The x-axis
shows the number of mobile. Mobile numbers 1 − 100 are voice users and 101 − 200 are
video users and 201 − 300 are FTP users. We see that the AUPC values are lower for
(Eb/No)j . Most importantly, we notice that the for the first 100 voice users, the values
converge around 5.0 dB and for the data users they are 3.0 dB which are the target values
desired in our simulation.
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Figure 8.10: OLPC and AUPC with respect to (Eb/No)j
8.2.5 Comparison of OLPC and AUPC with respect to Lj
In figure 8.11, we observe that the individual load the AUPC offers the system is
lower that the OLPC, indicating that this will maintain the noise rise in the system to a
better minimum than OLPC. the y-axis shows the values of Lj = 11+ W
(Eb/No)j .Rj.νj
and the
x-axis shows the number of Monte Carlo iterations.
8.2.6 Comparison of OLPC and AUPC with respect to Transmit Power
Pj
Here we compare the individual powers granted to the UEs for transmission. The
x-axis shows the individual values after 100 Monte Carlo simulations of the UEs and the
y-axis shows the power in Watts. We again observe that the AUPC converges to a lower
minimum that the OLPC. The relevance of this is that by keeping the individual values of
Pj to a minimum, the interference is limited as a result of which the spectral capacity of
the WCDMA system increases.
78
Figure 8.11: OLPC and AUPC with respect to Lj
Figure 8.12: OLPC and AUPC with respect to Pj
79
Figure 8.13: Comparison of Voice and Data Blocking Probabilities with and without PowerControl
8.2.7 Comparison of Voice and Data Blocking Probabilities with and
without Power Control
Figure 8.13 compares the voice and data blocking probabilities with and without
power control. Results from the Call Admission Control are compared to the results with
Call Admission Control and Power Control. The y-axis shows the percentage of voice and
data blocking and the x-axis shows the variation of voice erlangs. We observe that an
efficient radio resource management scheme that has a combination of resource control in
the upper layers and power control in the lower layers work best in minimizing the call
blocking and dropping probabilities.
80
Chapter 9
Location Updates of CellularNetworks Using Bloom Filters
Location Updates (LU) are e911 procedures mandated by the FCC for cellular
networks today; helping locate mobiles within 100 meters of their vicinity. This requires
paging all mobiles within a vicinity regularly thereby leading to an increased use of band-
width. This chapter analyzes the existing schemes of hash based paging in LU procedures
using Bloom Filters (BF) and introduces two new schemes to improve performance: Opti-
mization of Bloom Filters (OBF) and Cumulative Bloom Filters (CBF). An identifier field
in the paging message is coded by applying hashing functions to create a BF and this is
used to page a number of mobiles concurrently. False LU are the mobiles that may not
belong to a particular paging area but still respond with LU updates. We observe that
these false probabilities are very small and can be traded-off with the bandwidth gain. The
results obtained compare the analytical and simulation results and their observation leads
us to the goal of this research: to obtain a multi-fold increase in bandwidth gain at the cost
of keeping the false positives to a realistic minimum.
9.1 Introduction
In this section we introduce the concept of Bloom Filters, their simplicity in im-
plementation, the associated tradeoffs, its variations, their varied range of applications and
the basis for the union between cellular networks and BF.
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9.1.1 Bloom Filters
In 1970 Burton Bloom in [53] considered the problem of testing a series of messages
one-by-one for membership in a given set of messages. The idea is to allocate a vector v
of m bits, initially all set to 0, and then choose k independent hash functions, h1,h2,....,hk,
each with range 1,.....,m . For each element a ∈ A , the bits at positions h1(a), h2(a), ...,
hk(a) in v are set to 1. Given a query for b, we check the bits at positions h1(b), h2(b), ...,
hk(b). If any of them is 0, then certainly b is not in the set A. Otherwise we conjecture that
b is in the set although there is a certain probability that we are wrong. This is called a
‘false positive’ or a ‘false drop’. The parameters k and m should be chosen such that the
probability of a false positive is acceptable. False positives are possible, but false negatives
are not. Elements can be added to the set, but not removed unless the issue is addressed by
a counting filter [54]. The more elements that are added to the set, the larger the probability
of false positives.
Assuming that the hash functions are perfectly random, the probability of a false
positive for an element not in the set, or the false positive rate, can be calculated in a
straightforward fashion. The probability that one bit is set is given by Pset = 1/m and that
of it being unset is given by Punset = 1−1/m. For k transformations, Pk.unset = (1−1/m)k
and for n records Pnk.unset = (1 − 1/m)nk. After all n elements of A are hashed into the
Bloom filter, the probability that a specific bit is still 0 is given by:
(1− 1m
)kn ≈ e−kn/m (9.1)
If p = e−kn/m, the probability of a false positive becomes:
(1− (1− 1m
)kn)k ≈ (1− e−kn/m)k = (1− p)k (9.2)
Let f = (1− e−kn/m)k = (1− p)k. p and f are asymptotic approximations to represent the
probability a bit in the BF is 0 and the probability of a false positive respectively[55].
9.1.2 Variations of Bloom Filters
The main variations of BF are Counting BF, Compressed BF, Breadth BF and
Depth BF. These exist in literature today and their pros and cons are listed. This helps us
82
in understanding the reason why none of these variations can be used in LU and identifies
the need for the CBF which is introduced in this research.
Counting BF : Suppose that we have a set that is changing over time, with
elements being inserted and deleted. Inserting elements into a BF is easy; hash the element
k times and set the bits to 1. Unfortunately, one cannot perform a deletion by reversing
the process. If we hash the element to be deleted and set the corresponding bits to 0, we
may be setting a location to 0 that is hashed to by some other element in the set. In this
case, the BF no longer correctly reflects all elements in the set. To avoid this problem,
introduces the idea of a counting BF. In a counting BF, each entry in the BF is not a single
bit but instead a small counter. When an item is inserted, the corresponding counters are
incremented; when an item is deleted, the corresponding counters are decremented. To
avoid counter over flow, we choose sufficiently large counters. The disadvantage of counting
BF is the additional storage required to store these counters [54].
Compressed BF : If we choose the optimal value for k to minimize the false
probability as calculated above, then p = 1/2 . Under our assumption of good random hash
functions, the bit array is essentially a random string of m 0’s and 1’s, with each entry being
0 or 1 with probability 1/2. It would therefore seem that compression cannot gain when
sending BF. Mitzenmacher in [55] demonstrates the flaw in this reasoning. The problem
is that we have optimized the false positive rate of the BF under the constraint that there
are m bits in and n elements represented by the BF. Suppose instead that we optimize the
false positive rate of the BF under the constraint that the number of bits to be sent after
compression is z, but the size m of the array in its uncompressed form can be larger. It
turns out that by using a larger, but sparser, BF can yield improved false positive rates
with a smaller number of transmitted bits [55].
Breadth BF : If there is a tree T with j levels then the level of the root is level 1.
The Breadth Bloom Filter (BBF) for a tree T with j levels is a set of Bloom filters BBF0,
BBF1, BBF2,..., BBFi, where i ≤ j. There is one Bloom filter, denoted BBFi, for each
level i of the tree. In each BBFi, we insert the labels (attributes) of all nodes at level i.
Note that the BBFis are not necessarily of the same size. In particular, since the
number of nodes and thus keys that are inserted in each BBFi (i > 0) increases at each
83
level of the tree, we analogously increase the size of each BBFi. Let |BBFi| denote the
size of BBFi. BBF0, the final resulting filter is the logical OR of all BBFis.
The look-up procedure that checks whether a BBF matches with a path query
distinguishes between two kinds of path queries: path queries starting from the root level
and partial path expressions. In both cases, first the algorithm checks whether all attributes
in the path expression appear in BBF0. Only if we have a match for all the attributes will
the algorithm proceed to examine the structure of the path. Using BBF ’s in LU procedures
will lead to high amount of false positives and will also increase the computational overhead
[56].
Depth BF : Depth BF are similar to BBF and are mentioned in [56]. The look-up
procedure, that checks whether a DBF matches with a path query, first checks whether all
attributes in the path expression appear in DBF0. If this is the case, then the algorithm
continues treating both root-paths and partial paths the same. For a query of length p, every
sub-path of the query from length 2 to p is checked in the corresponding level according to
its length. If any of the sub-paths does not exist then the algorithm returns a miss.
9.1.3 Applications of Bloom Filters
The classical example of a BF is its use in dictionaries. For example, one might
use a Bloom filter to do spell-checking in a space-efficient way. A Bloom filter to which
a dictionary of correct words have been added will accept all words in the dictionary and
reject almost all words which are not, which is good enough in some cases. Depending on the
false positive rate, the resulting data structure can require as little as a byte per dictionary
word. One peculiar attribute of this spell-checker is that it is not possible to extract the
list of correct words from it at best, one can extract a list containing the correct words
plus a significant number of false positives. In this research, the focus in on applications in
wireless networks and the following references tells us the state of the art that exists in this
area.
It is widely used in many applications which take advantage of its ability to com-
pactly represent a set and filter out effectively any element that does not belong to the set,
with small error probability [57]. In [57], the authors introduce the Spectral BF (SBF), an
84
extension of the original BF to multi-sets, allowing the filtering of elements whose multi-
plicities are below a threshold given at query time. In [58] the use of BF in peer-to-peer
networks, resource routing, packet routing and measurement infrastructures is discussed.
The authors in [59] use BF to manage address cache management in wireless ad-hoc net-
works. [60] talks about collaborating web caches using BF as compact representations for
the local set of cached files. [61], [62] and [63] discuss how bloom filters are used in query
filtering and routing.
9.2 Location Updates and Bloom Filters
The Federal Communications Commission (FCC) mandated that carriers using
handset-based wireless location systems must provide the location of 911 calls to appropriate
public safety answer points (PSAPs) and be accurate to within 50 meters 67 percent of the
time and to within 150 meters 95 percent of the time. The network will page all the mobiles
within its boundary with a paging message occasionally with a Location Request (LR)
message and the mobiles will reply with a LU message. Mobiles must update the network
with their current location in order to have access. In addition to this some mobiles not in
the paging area will receive the message and reply with a location update message leading
to wastage of bandwidth. To cope with this, ideas are emerging that indicate the use BF
at the mobile side wherein the mobile on receipt of the paging message will detect if it is
being paged by using hash functions and checking the corresponding bit positions.
Figure 9.1 demonstrates location update procedures. The Base Station sends a
location request message to mobiles A and B. Mobile A resides within the paging vicinity
of the cell and mobile B is being served by another paging area. On receipt of the LR
message, the mobiles check the BF stored with the corresponding hash functions. If its
identifier bit is set to 1, it replies back with a LU message, else it does not acknowledge
the LR message. There is a lot of bandwidth needed to page areas that are huge or have a
large population of mobile users. Many mobile users reside on the edges of adjoining paging
areas. Applying BF to this technology leads to bandwidth gain which is a very important
and expensive resource in cellular networks.
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Figure 9.1: Location Request and Location Update
Very little work has been done in the area of using BF in LU or for other cellular
applications. This is an emerging idea and the coming years should see more use of BF in
cellular networks as BF applications are already inundating their contemporary network,
namely wireless ad-hoc networks. This section discusses in detail the one paper that exists
in this area and its pros and cons. The rest of the papers that do not add much value are
referenced.
In [64], the authors discuss how hash paged paging is used in location updates.
They have hash functions as described in 9.1.1 and set A = id1, id2,....., idn which are the
mobile ID’s in a paging area. A false positive in this case leads to a false location update,
but the authors claim that the space-efficiency of BF is achieved at a small cost of a ‘small’
false positive probability.
The strengths of this research is that they further take the analysis shown above
to relate it to the gain that is achieved in term of bandwidth utilization which is important
because the air-interface is a very expensive resource. In addition to this, they analyze it
in terms of queuing delays in the network. However, the drawbacks of this paper is that is
assumes the Mobile id’s and IPv6 addresses of 128-bits which is promising for tomorrow’s
networks but many of today’s networks have smaller identifier sizes like 32-bit or 64-bit id’s.
86
Further, the population density of the area in terms of mobiles per paging area used are
very small and they do not in any means represent the real-world scenario, where we may
have hundreds to thousands of mobile users per paging area.
[65] talks about probabilistic location from a peer-to-peer standpoint. [66] talks
about a hybrid BF for LU but from a more contention access point.
The idea in [64] is the foundation of this research. Based on this, this research
extends the idea into analytical and simulation modeling. The analytical modeling is com-
prised of Optimization of available BF parameters (OBF) and introduces the concept of
Cumulative Bloom Filters (CBF).
9.3 Analytical Modeling
In this section, we look at mathematically modeling a cellular network from a BF
point of view. The analytical computations consists of two parts: 1) Optimizing the number
of hash functions and 2) Using Cumulative Bloom Filters.
9.3.1 Optimization of Hash Functions (OBF)
From the above equations we observe that there are three important performance
metrics for Bloom filters that can be traded off:
1. computation time; based on the number of hash functions k
2. memory; based the size of the array m
3. probability of false positive f
Between these three parameters, optimization is useful and often exploited to suit
purposes depending on the application. Suppose we are given m and n and we wish to
optimize the number of hash functions k to minimize the false positive rate f. There are
two competing forces: using more hash functions gives us more chances to find a 0 bit
for an element that is not a member of A, but using fewer hash functions increases the
fraction of 0 bits in the array. The optimal number of hash functions that minimizes f as
87
Figure 9.2: Optimization of Bloom Filter
a function of k is easily found by taking the derivative. From equation 9.2 we know that
f = exp(k.ln(1 − e−kn/m)). If g = k.ln(1 − e−kn/m), minimizing the false positive rate f
can be found by taking the derivative of g with respect to k.
dg
dk= ln(1− e−kn/m) +
kn
m
e−kn/m
1− e−kn/m(9.3)
It is easy to check that the derivative is 0 when k = (ln2)(m/n); further efforts
reveal that this is a global minimum. k can now be minimized to:
kopt = (ln2) ∗ m
n(9.4)
In this case the false positive rate can be minimized to is F = (0.6185)m/n. k must
be an integer, and smaller k might be preferred since they reduce the amount of computation
overhead. Hence we have a tradeoff between the computation speed and the false positives.
In this research, computation speed is given importance and hence k is optimized. Figure
9.2 shows how Bloom filters are optimized to get a m bit BF for n elements from k hash
functions.
In this section of optimization, we have made two main assumptions: 1) All mobiles
are in the same cell and 2) All mobiles have the same service. But in the real world this is not
88
the case. We can exploit Bloom Filters for specific paging requirements. For example if all
the mobiles are divided into 7 paging areas or cells and further divided by service,i.e., voice,
video, FTP, HTTP. The following section tells us how BF are exploited in this research to,
for example, page all voice users in cell number 3.
9.3.2 Cumulative Bloom Filters
Cumulative Bloom Filter (CBF) borrows its ideas from multi-level BF introduced
in [56]. Multi-level BF’s are used as Breadth Bloom Filters (BBF) or Depth Bloom Filters
(DBF). In BBF, there are different levels of filters which may or maybe not be of the same
length and the final resulting filter is got by bitwise ORing all the bits of the filters. While
these filters are useful in many applications, as we shall see, the cumulative Bloom Filter is
a better approach for location updates in cellular networks. In figure 9.3 there are 3 filters
used. The first filter CBF1 is used for the cell. The minimum number of bits required for
this filter is the number of cells being simulated. The second filter CBF2 is used for the
classes of mobile service. The third filter is used as a regular BF for the mobile ID’s which
are 32 bit integers. The word ‘minimum’ is used here, because depending on the number of
mobiles in the paging area, all bits may be set to one rendering the CBF for a worst case
performance as in the previous section. For best performances, each of these CBFx should
be further optimized. From the results, a pattern is observed and curve-fitting procedures
are used to come up with a simple computational formula to optimize these CBFx’s.
Intuitively, we can see that as the number of CBF filters used increases, the false
positives decreases. As we will see from the results, this follows the pattern of an exponential
decay. The decaying is done at a rate proportional to the number of CBF filters and the
number of hash functions used in these filters. A quantity is said to be subject to exponential
decay if it decreases at a rate proportional to its value. Symbolically, this can be expressed
as the following differential equation, where N is the quantity and λ is a positive number
called the decay constant:
dN
dt= −λN (9.5)
89
The solution to this equation is:
N = Ce−λt (9.6)
This is the form of equation that is most commonly used to describe exponential decay.
The constant of integration C is often written N0 since it denotes the original quantity.
From equation 9.6, we get:
dN
N= −λdt (9.7)
Integrating 9.7, we get:
lnN = −λt + D (9.8)
lnN = Ce−λt (9.9)
where C = eD
If we have c number of CBFs in our design (CBF1, CBF2,....,CBFc) each having
kc hash functions and nCBFc bits, and if we have n number of mobiles in the paging area,
the rate of decay of the percentage of false positives can be given by:
f ≈ ne−(Pc
i=1 ki+Pc−1
i=1 nCBFi) (9.10)
where,∑c−1
i=1 ki = k1 + k2 + ...+ kc−1 and∑c−1
i=1 nCBFi = nCBF1 +nCBF2 + ...+
nCBFc−1
The last CBF is CBFc with kc hash functions and nCBFc bits, which is the
filter we have used in the optimization part of this research. The results of the CBF filter
will be as worst as the optimized BF. It is for this reason that in equation 9.10, the last
CBF filter which is based on the mobile identification bits is left out. This exponential
decay represents an estimation and/or prediction as to how varying the number of CBF
and its corresponding bits will have an effect on the false positives. This is an analytical
computation to be used for estimation/prediction purposes only while designing CBFs for
applications.
For future work, a good exploratory avenue would be one where Monte Carlo
simulations are used. The percentage of false positives can be fixed and at the end of each
Monte Carlo iteration, the number of bits required for each CBF can be found until the
desired false positive is reached.
90
Figure 9.3: Cumulative Bloom Filter
9.3.3 Performance Metrics
As we have seen so far, the major performance metrics in the use of BF is the
tradeoff between memory size, false positives and computation overhead. Since we have
kept the computational overhead to a simplistic minimum by optimizing the number of
hash functions, the focus will be on the array size m of the BF, the percentage of false
positives F , and the bandwidth gain G.
Using the standard paging procedure, the paging cost in each cell of a paging area
is one paging message per incoming call. In hash-based paging, the paging cost in each cell
is one paging message and several false location update messages per n incoming calls. If d
is the terminal density, i.e., the number of terminals per cell, then the paging cost in each
cell is 1 + d.F per n incoming calls. The bandwidth gain is then given by:
G =n
1 + d.F(9.11)
The density d depends on the cell size or paging area. d varies from small to large depending
on choice of pico, micro or macro cells which are differentiated by the size in paging area.
9.4 Simulation Modeling
In the case of optimization of BF, our simulation model consists of a single paging
area or a single cell, serving 10, 000 mobile users. Here, there is no distinction whatsoever
91
between the mobiles or their paging areas. To observe the changes in performance of gain,
G, the density d = 200 in all our simulations.
In the case of CBF, our simulation model consists of a center cell and 6 surrounding
hexagonal cells,i.e, a total of 7 cells. The service distribution of the mobiles are categorized
into 4 categories. This follows the Universal Mobile Telecommunications Systems (UMTS)
which is a popular third generation (3G) system has 4 service categories: Streaming (ex.
Video), Conversational (ex. voice), Background (ex. FTP) and Interactive (ex. HTTP).
Thus the CBF filters will have a minimum of 7+4+32 bits and should be optimized further
for best results.
9.5 Results and Discussions
In this section, the results based on the performance metrics identified in this
research are presented. The performance metrics of the BF size m, the percentage of false
positives f and the gain G are compared with the three schemes: Location updates in
Cellular networks using BF without optimization, with optimization and with optimization
and Cumulative Bloom Filters.
9.5.1 Without Optimization
Figure 9.4 shows the variation of the percentage of false positives with the size
of the BF m on the x-axis. Here n = 10, 000 and m is varied as x ∗ n. The first subplot
shows the entire range of values and the second subplot shows the values at a lower range
to observe the results of the number of hash functions more clearly. As mentioned in theory
above, we know that increasing the number of hash functions should lead to a decrease of
the percentage of false positives. But we see that this is not the case here. The values at
k = 4, show a smaller value of f as compared to the values at k = 6. Clearly, even though
the values do not differ by much, this is a disadvantage. Having more hash functions will
lead to a computational overhead. These results show a need for optimization of the design
when using BF.
Figure 9.5, shows the variation of gain, G, with the size of BF, m. As in the case
92
Figure 9.4: False Positives without Optimization
with false positives, we observe a better performance for k = 4 than k = 6 and hence this
further identifies the need for optimization. It must be mentioned here that the results
for both the false positives and the gain are obtained from simulations. As in the case
with every simulation, we are granting a degree of randomness in the system. To minimize
this randomness and to argue about the strength of these results, confidence intervals were
obtained. The results shown here are 95percent confidence interval means.
9.5.2 With Optimization
Having identified the need to optimize the BF depending on the number of hash
functions k and the number of mobiles n, this section presents results of hash based paging
with optimization of BF. Here m, the size of the BF is optimized since we know k and n;
m = k∗n/log(2). In figure 9.6, the y-axis shows the variation of percentage of false positives
and the gain for two cases: d = 20 and d = 200. The x-axis shows the variation of hash
functions k and the corresponding values of m. We observe from these results that the false
93
Figure 9.5: Gain without Optimization
positives decreases with increase in hash functions and the size of the BF. It is also observed
that the gain in both cases, when the density is 20 and 200 increase with increase in k and
m. It is interesting to note here that using BF for hash-based paging is more attractive as
cell sizes get smaller which is the case for many big cities and downtown areas.
Figure 9.7 shows three subplots, each comparing the analytical and simulation
values of false positives with varying m for when k = 2, k = 4 and k = 6. We observe from
these results that with increase in m, the simulations results match the analytical results.
However, when m is small there are variations in f . The entire range of results are plotted
here to make an overall statement. Due to this the values appear to be zero when they
actually are not.
9.5.3 With Optimization and Cumulative Bloom Filters
In this section we look at further improvement in performance of hash based paging
using Bloom Filters by comparing the results with optimization to those using Cumulative
94
Figure 9.6: False Positives without Optimization
Figure 9.7: Comparison of Analytical and Simulation Results
95
Figure 9.8: False Positives with Optimization and CBF
Bloom Filters with optimization. Figure 9.8 compares the percentage of false positives
with varying number of hash functions for the two schemes. We observe an increase in
performance when using CBF. The percentage of false positives decreases further when we
use CBF with optimization. These results are simulation results obtained from 95percent
confidence intervals.
Figure 9.9 shows the increase in gain got by applying the concept of CBF to
optimization. We see a huge improvement in performance. This is the performance metric
that is of utmost importance in this research. This improvement in performance is very
cost effective for cellular providers. The bandwidth gain obtained from using hash based
paging can be used to increase revenue by increasing the number of subscribers.
96
Figure 9.9: Gain with Optimization and CBF
9.6 Summary
This chapter has introduced the concept of BF and their various applications,
specifically those in cellular networks. The FCC mandated that carriers using handset-
based wireless location systems must provide the location of 911 calls to appropriate public
safety answer points (PSAPs) and be accurate to within 50 meters 67 percent of the time
and to within 150 meters 95 percent of the time. We have seen that though not much
work has been done in this area, there is a good potential for the same. We applied hash
paging using Bloom Filters to observe the improvement in bandwidth gain. The goal of this
research, which was to see an exponential improvement in bandwidth while keeping the false
positives to a realistic minimum, was obtained by applying the optimization and cumulative
bloom filter schemes. To strengthen the results presented in this chapter, confidence interval
means of simulation results were compared with analytical results.
97
Chapter 10
Conclusions and Future Work
In most of Europe and parts of the United States, third generation mobile in terms
of UMTS with WCDMA as its radio access interface is already a reality. For customers
already enjoying voice and data services via 2G and 2.5G, UMTS/WCDMA delivers faster,
more efficient cellular networks and with new possibilities. For many of the 1.2 billion
customers of second generation networks, UMTS is Third Generation mobile.
Using WCDMA as the air interface as its advantages and disadvantages. The
advantages being extended coverage and higher capacity and ability to support previous
generation systems. The disadvantages being the expensive radio spectrum in itself. To
make efficient use of the radio spectrum, many radio resource management schemes are
need to be implemented to make it worth the while to the cellular providers.The research was mainly divided into three parts:
1. Resource Allocation / Call Admission Control
2. Power Control
3. Location Updates for Cellular Networks
Chapter 1 discusses the motivation behind this research study, its questions and
its limitations. The background on the UMTS architecture and the WCDMA air interface
required to fully understand the research study is presented in chapter 2. The existing radio
resource management schemes, its limitations and the need for more efficient algorithms is
presented in chapter 3. Chapter 4 defines the methodology and the design implemented in
this research study. Chapters 5 and 6 define the simulation and the analytical models used.
In chapter 7, the results are presented and analyzed.
98
The simulation study was conducted in OPNET. OPNET was used in this study
because of the availability of the UMTS stack, propagation models and a very good design
of the air interface and mobility. Functionality changes were made in the Radio Network
Controller in order to implement algorithms of choice. A seven cell UMTS system was
studied. In order to limit complexity, only the first tier was evaluated.
We see in this research study, an attempt to study a multi-rate system with priority.
Most cellular systems have different classes of calls with varying arrival rates, varying service
rates and varying number of demands on the system. Each call has to be treated differently
in order to provide Quality of Service. This is what sets the difference between cellular and
ad-hoc networks. In addition to intense cellular coverage planning, efficient ways to handle
priority in a system is a must. The topic of multi-rate system with priority was identified
as a research problem, analyzed and solved. The observation of results show us that this
problem has been analyzed efficiently.
In addition to analyzing the system as a multi-rate system with priority, tier
analysis of the hierarchical cellular structure was analyzed. Where mobility is the most
important factor to be considered, the effect of handoffs and in turn the effect of ongoing
calls in a particular cell on its neighboring cells is an important issue that needs to be dealt
with. This was dealt with in this research study and the results show that the accuracy
was improved with this addition. This comprised of part one of the research.
Chapter 7 introduced the concept of fine-tuning certain Power Control parameters
and then adaptively choosing the transmit power of the UE to increase the spectral efficiency
of the WCDMA system, which is an expensive air interface. The advantage of such a scheme
is the simplicity of fine-tuning and Monte Carlo simulations. Adapting other parameters
other than the one chosen or adapting multiple parameters is an interesting topic of research.
Results showed that the Adaptive Uplink Power Control (AUPC) worked better in keeping
the power required for the UE to transmit lower than the existing schemes of Outer Loop
Power Control (OLPC). We saw that the Noise Rise and the load was also kept to a lower
minimum and hence we can conclude that using AUPC, we can keep outage to a lower
minimum. This comprised part two of the research.
Most importantly, we have seen that Call Admission Control (Part One) and Power
99
Control (Part Two) have worked in conjunction to further reduce the two most important
performance metrics in this research: the call blocking and call dropping probabilities.
Chapter 9 comprised part three of the research. It introduced the concept of
BF and their various applications, specifically those in cellular networks. The FCC has
mandated that carriers using handset-based wireless location systems must provide the
location of 911 calls to appropriate public safety answer points (PSAPs) and be accurate to
within 50 meters 67 percent of the time and to within 150 meters 95 percent of the time. We
have seen that though not much work has been done in this area, there is a good potential
for the same. We applied hash paging using Bloom Filters to observe the improvement in
bandwidth gain. The goal of this research, which was to see an exponential improvement
in bandwidth while keeping the false positives to a realistic minimum, was obtained by
applying the optimization and cumulative bloom filter schemes. This research specifically
introduced a new data structure called Cumulative Bloom Filters and further introduced
an idea to Optimize Bloom Filters. Working together, we from the results presented in
this chapter that the confidence interval means of simulation results were compared with
analytical results and the existing algorithms which showed an increase in bandwidth gain
while keeping false positives low and to a realistic minimum.
100
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Appendix A
Acronyms
3G Third Generation
3GPP Third Generation Partnership Program
ACAC Adaptive Call Admission Control
AAL ATM Adaptation Layer
ATM Asynchronous Transfer Mode
AUPC Adaptive Uplink Power Control
BBF Breadth Bloom Filter
BER Bit Error Rate
BLER Block Error Rate
BF Bloom Filters
BoD Bandwidth on Demand
BS Base Station
BER Bit Error Rate
BLER BLock Error Rate
CAC Call Admission Control
109
CBF Cumulative Bloom Filters
CDMA Code Division Multiple Access
CI Confidence Interval
CN Core Network
CS Circuit Switched
DBF Depth Bloom Filter
DRNC Drift Radio Network Controller
DS-CDMA Direct Sequence Code Division Multiple Access
Eb/No Energy Per bit to Noise Ratio
EDGE Enhanced Data-rates for GSM Evolution
ETSI European Telecommunications Standards Institute
FCC Federal Communications Commission
FDD Frequency Division Duplex
FER Frame Error Rate
FDMA Frequency Division Multiple Access
FTP File Transfer Protocol
GGSN Gateway GPRS Support Node
GMSC Gateway Mobile Switching Center
GPRS Global Personal Recovery System
GPS Global Positioning System
GSM Global System Mobile communications
110
HLR Home Location Register
HTTP Hyper Text Transfer Protocol
IMT International Mobile Telecommunication
ITU International Telecommunication Union
MAC Medium Access Control
ME/MT/MS Mobile Entity/Terminal/Station
MSC Mobile Switching Center
LU Location Update
LR Location Request
Node-B Node Base Station
NF Noise Figure
OLPC Outer Loop Power Control
OPNET OPtimum NETwork
PLMN Public Land Mobile Network
PS Packet Switched
PSAPs Public Safety Access Points
QoS Quality of Service
RAB Radio Access Bearer
RLC Radio Link Control
RNC Radio Network Controller
RRC Radio Resource Control
111
RRM Radio Resource Management
SBF Spectral Bloom Filters
SIR/SNR Signal-to-Noise Interference Ratio
SGSN Serving GPRS Support Node
SRNC Serving Radio Network Controller
TB Throughput Based
TDD Time Division Duplex
TDMA Time Division Multiple Access
ToS Type of Service
UE Universal Edge
UMTS Universal Mobile Telecommunications System
USIM Universal Subscriber Identity Module
UTRAN Universal Terrestrial Radio Access Network
VoIP Voice over IP
VLR Visitor Location Register
WCDMA Wideband Code Division Multiple Access
WPB Wideband Power Based