a novel traffic capacity planning methodology for lte radio network dimensioning

8/10/2019 A Novel Traffic Capacity Planning Methodology for LTE Radio Network Dimensioning

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Proceedings of ICCTA2011

A NOVEL TRAFFIC CAPACITY PLANNING

METHODOLOGY FOR LTE RADIO NETWORK

DIMENSIONINGJun Gu, Yufeng Ruan, Xi Chen, Chaowei Wang

ZTE Corporation Shanghai 201203, [email protected], [email protected], [email protected], [email protected]

Abstract

Traffic capacity planning is a challenging task inmultiple input multiple output (MIMO) &orthogonal frequency division (OFDM) based longterm evolution (LTE) cellular networks, resulted

from emerging diverse multimedia trafficrequirement, together with highly open & flexibleair interface design in LTE. In this paper, a newmethodology for dynamic real-time capacityplanning is proposed for LTE radio networkdimensioning, based on unified traffic processmechanism, fresh simulation methodology for airinterface, and smart self-evaluation andoptimization. By corresponding software designand implementation, it provides powerful tool forLTE network planners to get efficient, accurateand professional capacity planning outcome

without much manual effort.

Keywords: Long-term evolution (LTE);capacity

planning;traffic; simulation

1 Introduction

Since 2009, LTE technology has attracted greatinterests from top operators around the world.With the enhanced technical flexibility andimproved network capability, LTE shows to be thegreat momentum for the convergence of cellularnetwork and internet, which will bringrevolutionary transform of traffic pattern in

cellular networks. Traffic diversity, along with theflexibility and complexity of LTE air interface,bring the capacity planning for cellular networkinto new dilemma [1].

In traditional 2G/3G cellular network, circuitswitched voice traffic is the dominant service andErlang formula is the most popular and usefulmethodology to calculate network capacity [2],given specific network configuration informationand call blocking probability. When coming intomixed traffic dimensioning, with diverse resourcerequirement for different traffic type, knapsack

model for multiple traffic capacity planningbecame more popular under the assumption that

the network has fixed resource (number ofchannels in

GSM network, etc). In order to cope with thedynamic requirement in more advanced wirelesssystem, such as LTE, WiMAX, stochastic

knapsack is proposed, without considering theinfluence of packet level behavior[3][4]. In [5], asystematic capacity estimation methodology forsystem beyond IMT-2000 provides a way tocalculate resource requirement in packet switchbased wireless network, with the packet levelcharacteristics into consideration. In this method,the main drawback comes from the fact that it isunder the assumption of fixed spectrum efficiencyfor given scenario, which in fact is not the case dueto the interplay among traffic characteristics,algorithm behavior and network performance.

In LTE, due to the introduction of variousadvanced link level and system level techniques,such as flexible bandwidth, OFDM, MIMO, inter-cell interference coordination (ICIC), inter-cellpower control (PC), frequency domain fastscheduling (FDFS), the network enables greatflexibility in higher date rate provision and betterquality of service (QoS) guarantee[1][6][7], but atthe same time, it is more complex to gain preciselyquantitative insight into system capability underdifferent service provision scenarios, especially forpractical LTE network deployment, which requiresprofessional capacity planning for improved user

experience and reduced cost.

Generally, capacity planning is the process to

determine network topology and configuration(number of site, MIMO configuration, basic radio

parameter determination, etc), under theconstraints of service requirement based on trafficgrowth prediction; in addition, operators strategy

should be taken into consideration with highestpriority before concrete network design. Accordingto previous illustration, due to the strong inter-action of different aspect in LTE cellular networks,traditional methodology for capacity could betaken to dynamic LTE system, even if with someenhancement.


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Therefore, for LTE network, system levelsimulation became more and more important dueto its capability in investigating systematic

performance, with specific modeling method undercertain evaluation target. Among the three popularsimulation methods (static, semi-static, dynamic),

dynamic system simulation is the most powerful inrevealing accurate system performance. But due tohighly complex modeling and low efficiency, it isprohibitively difficult for network planners to usesuch platform to search for optimum capacityplanning result.

In this paper, a novel system simulation designbased LTE radio network capacity planningmethodology is proposed for intelligent optimumresult search, given specific traffic information. Itincludes key components of unified trafficprocessing, flexible air interface adaptation and

simulation and Smart evaluation & optimization.And specially, for air interface simulation,probabilistic interference modeling and usermapping transform multi-cell simulation intosingle cell simulation while keeping systemcharacteristics, with dramatic reduction onsimulation complexity.

2 Methodology framework illustration

The overall framework of the proposed LTE radionetwork capacity planning methodology is shownin Figure 1. Generally, this methodology is based

on innovative iterated system simulation to findoptimum capacity planning solution, based onspecific traffic requirement.

Figure 1 LTE capacity planning methodology

For the input part, it consists of two main elements:1) traffic requirement input: in order to achieve

instructive capacity planning outcome, a completeset of information about traffic prediction in future

network should be provided, scenario definition(Dense urban, urban, suburban, rural, and so on),population distribution (population density in eachscenario), service penetration ratio, traffic type,traffic arrival density, traffic QoS requirement, etc.In addition, the definition should be taken

operators specific strategy into consideration withhighest priority, which is of vital importance to thequantitative definition of above traffic relatedrequirement. 2) Parameter input: two types ofparameters are defined. First class is the basicradio and engineer parameters, which includesSystem bandwidth, transmission power (totalpower, power allocation/control parameter),antenna type/pattern, MIMO configuration, and soon. The second class is optimization parametersand tuned to search for optimum capacity planningsolution. Typically, such class mainly refers tointer-site distance and antenna downtilt, and for

more advanced planning, some radio parameters(power parameter, bandwidth parameters, etc)could be chosen as optional optimizationparameters.

Before iterated optimization, a unified traffic

process module is design to transformaforementioned diverse traffic requirement intouniform form format for convenient air interfacesimulation. In this procedure, the complex servicerequirement from operator is translated intosimultaneous online user number and QoSrequirement for each traffic type and then such

information is put into the iterated simulationprocess.

Iterated dynamic simulation based optimizationprocess is the key of proposed LTE capacityplanning methodology. After determining theunified traffic requirement and basic engineeringand radio parameters, the optimization parametersshould be initiated. Then optimization target is setand the whole iteration process starts, which isfollowed by dynamic simulation and smartoptimization.

In the dynamic simulation part, firstly, real-timetraffic data is generated based on the informationreceived from unified traffic processing module,and the output traffic packet is storage in databuffer for scheduling. Then CINR modeling anduser mapping the two core components are carriedout. By multi-cell topology and wrap aroundrelationship construction, together with largeamount of randomly dropped CINR collectionusers (CCU), CINR distribution is collected innetwork wide basis. The CINR collection processis aided by random inter-cell interference selection

technique, and provides near-to-fact CINR

distribution under predetermined network topologyand system parameters, without the need of


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scheduling. Based on the network CINRdistribution, and traffic user number in one cellcalculated from total online user number & site

number in current iteration, CINR mapped trafficusers (CMTU) are linked to specific CINR valuesby probabilistic mapping according to CINR

cumulative density function (CDF). By such novelprocessing, the simulation of one cell could be arepresentative of the whole network in averagesense, hence it is reasonable to execute single cellsimulation during iteration. Such simplificationbrings remarkable computation burden withoutmuch performance loss. After the CINR collectionand user mapping, fast scheduling is execute ontransmission time interval (TTI) basis, and thenlink throughput is determined from the relationshipwith resource block (RB) number and CINRthrough link level simulation under specificchannel model.

In order to obtain optimum capacity planningsolution, above dynamic system level simulation isperformed in iterated way, with automatedevaluation and optimization parameters settingbefore next iteration. The self-evaluation processdecides whether or not the previous optimizationparameters fulfill capacity planning requirementunder predetermined conditions; the automatedsearch is in charge of optimization setting for nextround simulation and evaluation. In the self-

evaluation process, when the optimum solution isfound, iteration stops and capacity planning results

are delivered.

3 Unified traffic processing

In LTE, two class of traffic are defined as shown inTable 1: guaranteed bit rate (GBR) and non-GBR,and classified into 9 QCIs, with diverserequirement on priority, packet delay and packetloss rate, and impose different requirement onradio resource management during resourceallocation. Some traffic examples for each type arelisted in the table [1].

Table 1 QoS Class Identifiers (QCIs) for LTE

During LTE network capacity planning process,traffic model varies greatly. The simplest andconvenient way is to get complete information

about user density, traffic type/QoS requirement,traffic mix pattern, call arrival density, etc. Asample service requirement table is provided in

Table 2. And such set of information could betranslated into simultaneous online user numberand related QoS requirement for subsequent.

Table 2 Traffic requirement example

However, in many network planning cases, we

could only get rather rough information for servicerequirement, and certain degree enhancement andremodeling should be done before used forsimulation process:1) If only aggregate throughput requirement is

provided, set of virtual full buffer traffic usersshould be modeled. In this case, no explicitQoS requirement is imposed on user andduring iteration, the data rate constraint is oncell basis, the modeled user number andattached full buffer property would bedelivered to air interface simulation.

2) If very rough traffic properties (user density,

traffic type, etc) are provided, typical QoSsettings for each type of traffic could be usedas default input. After such remedy andsimultaneous online user number calculation,the traffic information is transfer to simulation.

3) If complete traffic model information isprovided, after simultaneous online usernumber calculation, the traffic information isdelivered to simulation.

After the processing, the traffic requirement isinput into iterated simulation with uniform pattern:simultaneous online user and marked QoS

requirement for each type of traffic. Therefore,when carried out iteration, no matter what of traffic(even non-type-service is included), the simulationcould be executed in a unified way without anychange to adapt to diverse input.

4 Flexible air interface and simulation

After finished unified traffic processing,

simultaneous online user number and associatedQoS requirement are delivered for dynamic

iterated system simulation, with uniform format.Then simulation starts for optimum capacity

planning solution search.


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4.1 Simulation parameter setup

Before integrated simulation, two types ofparameters should be setup:

1) Basic parameter

This class parameter mainly consists of the

basic network configuration such as systembandwidth, transmission power, antennaconfiguration, MIMO mode, ICIC mode, etc.When entering into integration, such

parameters keep constant.2) Optimization parameter

This type of parameter typically includesnetwork topology related configuration: inter-site distance (ISD), antenna downtilt, etc. Foradvanced capacity planning process, otherparameters could be chosen optionally for getmore intensive planning results. Suchparameters are determined automatically by

Automated Search module according to lastround simulation results evaluation, based onsmart search mechanism.

4.2 Traffic model adaptation

The aim of traffic model adaptation module is toadjust simultaneous online user number and thebehavior of generated traffic packet.

1) User number adaptation

For each round of system simulation, a typicalnetwork topology configuration is the ISD andwhen different ISD is set, according to given

user density in predetermined planning area,user number in each cell could be calculatedbased on number of cell number derived fromsingle cell covered area. CMTU generation isin accordance with the calculated user number.Specifically, such user number is exclusivelyused for dynamic traffic simulation and has norelationship with the collection of CINRdistribution.

2) Traffic model adaptationWhen number of simultaneous online cellusers is derived in 1), based on which CMTUsare generated. According to previous

illustration, different types of trafficrequirement would be delivered. In this part,randomly distributed packet size and arrivalprocess is explicitly modeled based on traffictype and associated QoS parameters. Thegeneration of data packets is carried in a real-time fashion and the formed packets are putinto radio link layer (RLC) buffer forscheduling. With effectively transmitted datamount/data rate/delay/jitter detection andcollection, the statistics of traffic requirement

satisfaction could be provided and used asindicators for optimization parameter update

and judgment of capacity planning solutionquality.

4.3 CINR modeling

Carrier to interference and noise ratio (CINR)modeling is the key process for determiningaccurate statistic signal quality distribution innetwork wide sense, which requires a certainnumber of randomly dropped users in each for

covered area, especially for uplink because theinterference for uplink comes from terminal, wherethe power is generated. Traditionally, the sameuser set if generated for CINR collection and

traffic simulation, which require large number ofsnapshots based simulation to get averaged resultsunder specific scenario and parameter setting.

In this paper, a novel simulation process

independent CINR modeling methodology isproposed to get reliable CINR distribution andsimplify subsequent dynamic simulation, with onlyone round simulation is necessary for each time of

iteration.

In the propose scheme, large number of trafficdependent user are randomly distributed overmodeled area, and then:

1) For downlinkIn each cell, for every user, reference signalreceived power is calculated based onpropagation model, then interference isderived base on system load set, with wraparound technique, and finally CINR for each

user could be calculated. Based on the user

level CINR value, network level CINRdistribution is formed. Generally, CINR

collection process is similar to traditional art,except for the principle for user number

setting over the concerned area.2) For uplink

In the propose method, due to the

independence of CINR collection anddynamic simulation process, there is no priorinformation on frequency resource occupationstatus, which is compulsory to decide whetheror not specific neighboring cell generates

interference upon serving cell, and from which

user, and hence randomized interfererselection (RIS) method is designed to solve

this problem.

RIS methodology design complies withnetwork interference characteristic in statisticsense, resulting from the random frequencyhopping behavior by dynamic scheduling. Inthis scheme, for every power controlled userin each serving cell, after received powerspectrum density (PSD) is calculated, aninterferer is randomly selected in neighboringcell, with interference strength scaled by

system load. Then all interferences aresummed up for derivation of user level CINR.


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The RIS methodology could reveal ICICimpact by imposing constraints on flexibilityof interferer selection. All CCUs CINR

values are collected to form network CINRdistribution.

4.4 User mappingUser mapping is the process of CINR valueassignment for each terminal. In the CINRmodeling module, the network level CINRdistribution is collected through CCU behaviormodeling. Based on the collected CINR CDF, thesimultaneous online users are mapped to verticalaxis in uniform distributed manner. Specially, suchmapping process is only needed in cell basis andthe subsequent single cell simulation would be

effectively represent system level behavior whilelargely reduce simulation burden compared with

traditional simulation methodology.

4.5 Traffic simulation

In this part, traffic simulation is carried out in TTIbasis. In each TTI, users are ordered based onassociated CINR value, QoS requirement andbuffer status and scheduled according to specificscheduling principle and criteria. Then certainnumber of RBs is allocated to each user for datatransmission. Link level performance is modelingthrough throughput vs. SINR mapping based onscenario based link level simulation.

5 Smart evaluation and optimization

During the iterated simulation, smart evaluation

and optimization update behave as two keyelements for planning result quality and processefficiency. Smart evaluation is implemented to

investigate simulated performance after each roundsimulation, and decide whether or not fulfilling

capacity planning target under allowable deviation.If predetermined target is achieved, iteration wouldbe stopped and capacity planning results are

delivered for engineering usage.

If capacity planning target is not satisfied undercurrent optimization parameter configuration,another round of simulation would be executed,

before which new set of optimization parametershould be automatically configured. Determinationof new parameters is highly dependent on previousperformance evaluation. Based on the gap betweensimulated performance and input requirement,parameters for next round simulation are setautomatically with assistance of fast searchalgorithm.

Based on the flexible air interface modeling,

dynamic traffic simulation is carried in iterated

fashion to search for optimum capacity planningresults, with updated network parameterconfiguration in each round of simulation.Specifically, the independent modeling for CINRdistribution and traffic simulation enable networklevel averaged single cell simulation, which

dramatically reduces system complexity, withoutmuch loss for simulation capability.

6 Conclusions

In this paper, a novel LTE network capacityplanning methodology is proposed for flexible,accurate, and efficient network dimensioning,based on unified traffic processing and iteratedsimulation. For the simulation part, new networkmodeling method dramatically improves systemefficiency and enables fast implementationengineering usage. In addition, the intensively

modeled simulation platform could also be utilizedfor theoretical research for LTE capacity planningrelated topics.

References

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[4] M.Jaber, S.A.Hussain, A.Rouz, DownlinkErlang Capacity of Cellular OFDMA,FUJITSU Sci.TECH, 2002.

[5] J.Huschke, T.Irnich, A.Lappetelinen,Methodology for estimating the spectrumrequirements for further developments ofIMT-2000 and systems beyond IMT-2000,WINNER project, 2005.

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a novel traffic capacity planning methodology for lte radio network dimensioning

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