integrated downlink resource management for multiservice wimax networks
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Integrated Downlink Resource Management for Multiservice WiMAX Networks. Bo Rong, Yi Qian, and Kejie Lu University of Puerto Rico IEEE Transaction on Mobile Computing. Outline. Introduction WiMAX OFDMA TDD system Integrated APA-CAC Downlink Resource Management Framework - PowerPoint PPT PresentationTRANSCRIPT
Integrated Downlink Resource Management for Multiservice WiMAX Networks
Bo Rong, Yi Qian, and Kejie Lu
University of Puerto Rico
IEEE Transaction on Mobile Computing
Outline Introduction WiMAX OFDMA TDD system Integrated APA-CAC Downlink Resource
Management Framework Downlink APA Optimization Downlink CAC Optimization Simulation Results Conclusions
Introduction To handle heterogeneous traffic load in a WiMA
X network Efficiently allocate resources to different subscribers
and applications
Radio power Determine the aggregated downlink data rate of ea
ch subscriber Adaptive power allocation (APA)
Access bandwidth assigned to different applications in a subscriber’s local network. Call admission control (CAC)
Introduction
APA Produce high revenue for service providers Keep most users satisfied.
CAC Requirement of WiMAX subscribers A policy
Good tradeoff between service providers and subscribers
WiMAX OFDMA TDD system
WiMAX OFDMA TDD system-Full Usage of SubChannel
OFDMA mode of 2048 subcarriers, NE is 32
Integrated APA-CAC Downlink Resource Management Framework
Downlink APA Optimization
Develop a fairness-constrained greedy revenue algorithm Maximize the revenue of service provider Provide fairness amongst all subscribers
ExampleBS
SS SS SS SS
1,2,3,…,M classes of traffic load
class i traffic 1. requests arrive from a random process with average rate λi
2. demands bi bandwidth resources3. average connection holding time is 1/μi seconds
ExampleBS
SS SS SS SS
1,2,3,… j
Example
Power Revenue
downlink datatransmission rate
Subscriber can not get the bandwidth it demands
Selected subcarrier
Algorithm Power constrain and Fairness threshold
K subscriber
subcarriers
Initialization
Required power to transmit b b/s/Hz on subcarrier J
downlink traffic rate
downlink bandwidth capacity
Total potential revenueof the given subscribe
Request arrive from random process
Avg. connection hold timedownlink data
transmission rate
Traffic rate
Transmission rate
Required power to transmit b b/s/Hz on subcarrier J
Downlink CAC optimization CAC is used to accept or reject connection req
uests State information QoS requirements of these connections.
Brute force searching Straightforward method to achieve the optimal solut
ion. Unbearable complexity:O(B2M)
off-line scenarios
Design Criteria
Optimal revenue criterion long-run average
Optimal utility criterion
Number of connections
steady state probability that the systemis in state
Bandwidth requirement
CP structured admission control policy
Complete partition policy allocates each class of traffic a certain amount of non-overlapping bandwidth a CP policy can be decomposed into MM independ
ent sub-policies A class i connection request will be accepted if a
nd only if there is enough free bandwidth in BiCP
Greedy Approximation Algorithm
load
Erlang B formula Erlang is a unit of traffic measurement. Erlang B formulation
Calculate the probability that a resource request from the customer will be denied due to lack of resources.
Greedy Approximation Algorithm
CP*:optimal CP policy with maximum revenue
Load carried in a M/M/N/N queuing system
Greedy Approximation Algo. for CP*
Blocking prob.
Utility-constrained Greedy Approximation Algorithm for CPU∗
CP+: CP policy of maximum utility CPU*:optimal CP policy with maximum
revenue under the utility constrains
With constrains
Without constrains
Simulation results Downlink APA optimization in OFDMA-FUSC mo
de of 32 subscribers 2 to 10 km
1024 subcarriers 10 kHz
x=80 revenue rate,
rerUGS = 5 rerrtPS =2 rernrtPS = 1 rerBE = 0.5
Fairness constraint Fth = 80%.
Simulation results
Traffic load PPBE: uniformly distributed in [10%, 30%] PPUGS : uniformly distributed in [10%(1-PPB
E), 30%(1-PPBE)] PPrtPS : uniformly distributed in [20%(1-PPB
E), 60%(1-PPBE)] PPnrtPS: (1- PPBE - PPUGS - PPrtPS )
APA revenue/ Potential revenue
Out of fairness %
Overall performance
Normalized revenue or utility-brute force
Greedy
Overall performance APA optimization:
APA1: equal power allocation criterion APA2: pure greedy revenue algorithm; APA3: fairness-constrained greedy
revenue algorithm CAC optimization:
CAC1: complete sharing (CS) policy; CAC2:greedy approximation algorithm
for CP∗ CAC3:utility-constrained greedy
approximation algorithm for CPU∗;
Conslusion Downlink resource allocation problem in
WiMAX networks APA and CAC optimization problems Demands of both WiMAX service providers a
nd subscribers are considered. Simulation study demonstrates
Requirements of service providers and subscribers can be satisfied