A Resource-estimated Call Admission Control

Algorithm in 3GPP LTE System ⋆

Sueng Jae Bae1, Jin Ju Lee1, Bum-Gon Choi1, Sungoh Kwon2, and Min YoungChung1⋆⋆

1School of Information and Communication EngineeringSungkyunkwan University

300, Chunchun-dong, Jangan-gu, Suwon, Kyunggi-do, 440-746, Korea2Telecommunication R&D Center

SAMSUNG ELECTRONICS416, Maetan-dong, Youngtong-gu, Suwon, Kyunggi-do, 443-742, Korea

Abstract. As the evolution of high speed downlink packet access (HS-DPA), long-term evolution (LTE) has being standardized by the 3rdgeneration partnership project (3GPP). In the existing mobile commu-nication networks, voice traffic is delivered through circuit-switched net-works, but to the contrary in LTE, all kinds of traffic are transferredthrough packet-switched networks based on IP. In order to provide qual-ity of service (QoS) in wireless networks, radio resource management(RRM) is very important. To reduce network congestion and guaranteecertain level of QoS for on-going calls, call admission control (CAC), inpart of RRM, accepts or rejects service requests. In this paper, we pro-posed resource-estimated CAC algorithm and evaluated the performanceof the proposed CAC algorithm. The result shows that the proposed al-gorithm can maximize PRB utilization and guarantee certain level ofQoS.

Key words: LTE System, CAC, QoS, RRM

1 Introduction

LTE has being standardized by 3GPP as part of 3GPP release 8 [1]. By adopt-ing orthogonal frequency division multiple access (OFDMA) and multiple-inputmultiple-output (MIMO) technologies, LTE increases data rate and improvesspectral efficiency [2]. In addition, since LTE evolves from HSDPA, it can beeasily compatible with the current mobile communication networks [3]. In the ex-isting mobile communication networks, voice traffic is delivered through circuit-switched networks, but in LTE, all kinds of traffic, such as voice, streaming,data, etc., are transferred through packet-switched networks based on IP.

⋆ This work was partially supported by Samsung Electronics and the MKE(The Min-istry of Knowledge Economy), Korea, under the ITRC(Information Technology Re-search Center) support program supervised by the IITA(Institute for InformationTechnology Advancement) (IITA-2009-C1090-0902-0005).

⋆⋆ Dr. M.Y. Chung is the corresponding author.

2 Sueng Jae Bae et al.

In order to provide QoS for various kinds of services in wireless environments,RRM is very important [4]. To reduce network congestion and guarantee certainlevel of QoS for on-going calls, CAC, in part of RRM, decides acceptance orrejection of service requests. Evolved universal terrestrial radio access networknode B (eNB), base station in LTE system, may perform CAC as several con-ditions, such as channel status, QoS requirements for requested services, bufferstate in eNB, and so on [1][5].

The existing CAC algorithms can be classified into two categories, static anddynamic. Static CAC algorithms reserve resources for handoff calls [6][7][8][9].However, channel reservation method may cause lower spectral efficiency [10].Dynamic CAC algorithms perform admission control through estimation of radiochannel state and available resources [11][12][13]. Since dynamic CAC algorithmsassume that all requested calls have the same QoS requirement, they can notdirectly adapt to LTE system which provides various kinds of services.

In this paper, we propose a resource-estimated CAC algorithm. Whenevera service request occurs, the resource-estimated CAC algorithm estimates thenumber of Physical Resource Blocks (PRBs) required for the service request.Based on the service type and modulation and coding scheme (MCS) level ofuser,the number of required PRBs is determined. Since the resource-estimatedCAC algorithm considers minimum data rate required for the requested service,it can maximize the utilization of physical resources. We conduct intensive sim-ulation in order to evaluate performance of the proposed CAC algorithm. Therest part of this paper is organized as follows. Section 2 describes existing CACalgorithms. The proposed CAC algorithm is discussed in Section 3. In Section4, we analyze the performance of proposed algorithm. Finally, conclusions arepresented in Section 5.

2 Pre-studied CAC Algorithms

The existing CAC algorithms can be divided into static and dynamic. StaticCAC algorithms reserve resources for handoff calls [6][7][8][9]. Dynamic CACalgorithms perform admission control through estimation of radio channel statusand available resources [11][12][13]. In this section, we explain existing threestatic CAC algorithms, guard channel, fractional guard channel, and queueingprinciple. In addition, we illustrate existing three dynamic CAC algorithms, localpredictive, distributed, and shadow cluster.

2.1 Static CAC algorithms

Guard channel algorithm reserves some channels among total number of channelsfor handoff calls [6]. Admission control procedure of guard channel algorithm issimple and its implementation is easy. However, in the guard channel algorithm,it is very difficult to determine the number of channels reserved for handoffcalls because arrival patterns of handoff calls are changed as the movement of

Resource-estimated Call Admission Control Algorithm in 3GPP LTE System 3

users. Moreover, the guard channel algorithm may decrease utilization of physicalresources as the number of reserved channels increases.

To improve resource utilization of the guard channel algorithm, Ramjee et al.proposed fractional guard channel algorithm [7]. The fractional guard channelalgorithm determines an acceptance or a rejection of new calls with decisionprobability varied as the number of busy channels. In case that channels aresufficiently available, handoff calls are accepted, but new calls can be rejected.Thus, in the fractional guard channel algorithm, dropping probability of handoffcalls may be smaller than that of new calls. In addition, since decision probabilityvaries as the number of available channels, the fractional guard channel algorithmalleviates congestion in the network. However, channel reservation schemes, suchas guard channel and fractional guard channel, may use inefficiently wirelessresources [10]. Moreover, channel reservation schemes may excessively block newcalls compared with handoff calls, because they always reserve some channels forhandoff calls [14].

To overcome disadvantages of channel reservation schemes, queueing prin-ciple algorithms were proposed [8][9]. In these algorithms, call requests are ac-cepted when there exist available channels. However, if all the channels are un-available, new and handoff calls are registered in the waiting list as their queueingdiscipline. When channels go into idle state due to call release or handoff, theyare allocated to the call with the highest priority in waiting list.

2.2 Dynamic CAC algorithms

Local predictive CAC algorithm predicts resource in local base station [11]. Thelocal predictive CAC algorithm estimates the amount of resources required fora serving call based on Wiener processes. In addition, it predicts arrival timesfor handoff calls and then reserves resources for the handoff calls. The localpredictive CAC algorithm has lower dropping probability for handoff calls thanthat for new calls because of adaptively preserving wireless resources for handoffcalls. However, to correctly predict arrival times and required bandwidth ofhandoff calls, information on handoff calls should be shared with base stationsrelated with the handoff calls.

Naghshineh et al. proposed distributed CAC algorithm which predicts localresources as well as resources of adjacent cells [12]. The distributed CAC algo-rithm considers the number of handoff calls moving from adjacent cells and theirQoS requirements. The distributed CAC algorithm guarantees the QoS of hand-off calls more effectively than the local predictive, because neighbor base stationsexchange information on handoff calls, such as arrival rate, required bandwidth,etc. However, the distributed CAC algorithm assumes that all calls have thesame service type and QoS requirement. In multimedia wireless networks, thereexist various kinds of service calls and their QoS requirements may be different.Thus, the service types of calls should be reflected on CAC algorithm.

Shadow cluster CAC algorithm, one of distributed CAC algorithms, performsadmission control considering movement of UEs, i.e., velocity, movement direc-tion, and position in cell of UEs [13]. According to movement of user equipment

4 Sueng Jae Bae et al.

(UE), the base station which UE belongs to selects adjacent cells, named forshadow cluster, that UE possibly moves to. The base stations of adjacent cellsselected as shadow cluster reserve on an amount of resources for handoff calls.However, the shadow cluster CAC algorithm may incur overhead because allbase stations should have information on the movement of UEs.

3 Proposed CAC algorithm

Resource-estimated CAC algorithm estimates the number of PRBs which shouldbe allocated to the requested call. The number of required PRB should be de-cided by reflecting the type of the requested service and current MCS levelof UE. In addition, the resource-estimated CAC algorithm calculates availablePRBs based on PRB usage of on-going call measured by eNB. Fig. 1 illustratesthe flow chart of call admission control procedure in the resource-estimated CACalgorithm.

,PRB

MCS

reqPRB

reqB

BN =

?PRB

req

PRB

free NN >

PRB

NRT

PRB

RT

PRB

total

PRB

free NNNN −−=

Fig. 1. Flow chart of call admission control procedure in the resource-estimated CACalgorithm

In Fig. 1, NPRBreq , Breq, and BPRB

MCS denote the number of required PRBs perone second, required data rate, and the number of bits carried in a PRB under thecurrent MCS level of an UE requesting a service, respectively. Since transmissiondata rate in wireless environment varies as channel condition, NPRB

req is calculated

as Breq over BPRBMCS . Breq is determined as the service type of the requested call.

BPRBMCS is calculated as channel quality indicator (CQI) information reported

from the corresponding UE through physical uplink control channel (PUCCH) orphysical uplink shard channel (PUSCH). In general, handoff call requests occur

Resource-estimated Call Admission Control Algorithm in 3GPP LTE System 5

when corresponding UEs cross over cell boundary. Thus, for handoff calls, weuse the smallest BPRB

MCS among possible BPRBMCSs under the given cell environment.

NPRBfree denotes the total number of available PRBs during the past one second.

To find NPRBfree , eNB calculates PRB usage of on-going calls at arrival time of a

service request. NPRBRT and NPRB

NRT denote the number of PRBs during the pastone second that eNB actually allocates to real-time services and non real-timeservices, respectively. The total number of PRBs per second, NPRB

total is decidedas the channel bandwidth of LTE system. Thus, NPRB

free is easily obtained by

subtracting the sum of NPRBRT and NPRB

NRT from NPRBtotal . If NPRB

free is bigger than

NPRBreq , the requested call is accepted. Otherwise, the requested call is rejected.

Since resource-estimated CAC algorithm only estimates minimum data rate ofthe requested service, it can be easily implemented.

4 Performance Evaluation

We develop event-driven simulator for 3GPP LTE downlink system using C++.To evaluate performance of the proposed CAC algorithm, we consider a radioaccess network consisting of seven hexagonal cells. Radius of each cell is 250mand identification numbers of cells are 0 to 6, as shown in Fig. 2. In addition, weassume proportional fair (PF) scheduling scheme as MAC scheduling algorithm[17]. We consider an OFDMA system with 5 MHz of downlink channel bandwidthwhich is one of the channel bandwidths specified in LTE system. For wirelesschannel conditions, path-loss and multi-path fading are considered but inter-cellinterference is not reflected on our simulation. To determine the MCS level ofan UE, we use modified COST 231 Hata model which reflects 10 dB log-normalshadow fading[15]. The number of UEs is 1750 and their positions are uniformlydistributed in seven cells at the starting time of simulation. The mobility modelis considered as random-walk model. The velocities of all UEs are assumed tobe 4km/h, and flight time of all UEs is uniformly distributed between 10 secand 20 sec. Service requests arrive at eNB as Poisson processes with parameterλ and service time is determined by an exponential distribution with mean 1/µ.The simulation time is 10,000 sec and statistical information between from 0sec to 2,000 sec is ignored. eNB has a logical queue per service of an UE with10MBytes. The simulation parameters are described in Table 1.

For simulations, we consider four service types, FTP, web, video, and VoIP.When user requests a service, service type is uniformly selected among four ser-vice types. The traffic mixture ratio of FTP, web, video, and VoIP is considered25:25:25:25, and their characteristics are given in Table 2 [15][16].

As performance measures, the average data rate, the average packet delay,PRB utilization, blocking probability of new calls, and dropping probability ofhandoff calls are considered. The average data rate is defined as the ratio oftotal amount of bits, sent by the eNB to all UEs during the simulation time.The average packet delay is considered as the sum of mean packet transmissiontime and mean queue-waiting time. PRB utilization is defined as the ratio ofthe number of PRBs allocated to UEs during the whole simulation time. The

6 Sueng Jae Bae et al.

Fig. 2. Cell structure in simulation

Table 1. The simulation parameters

Parameter Value Note

Number of cells 7Radius of cell 250m hexagonal shapeNumber of UEs 1750Velocity of UEs 4km/hFlight time of UEs [10, 20] sec uniform distributionDownlink channel bandwidth 5MHz 50 PRBs per TTITraffic mixture ratio 25:25:25:25 FTP:web:video:VoIPSimulation time 10,000 sec 0–2,000 sec is ignoredService duration 180sec exponential distributionQueue length 10MBTTI 1ms

Resource-estimated Call Admission Control Algorithm in 3GPP LTE System 7

Table 2. Characteristics of traffic considered for simulation

QoS class Service Component Statistical ParametersCharacteristics

Best effort FTP File size Truncated Mean: 2MBlog normal Std.dev.: 0.722MBdistribution Max: 5MB

Interactive Web Number of Log normal Mean : 17data browsing pages per session distribution Std.dev.: 22

(HTTP) Main object size Truncated Mean: 10710Byteslog normal Std.dev.: 25032Bytesdistribution Max: 2MB

Min: 100BytesEmbedded object Truncated Mean: 7758Bytessize log normal Std.dev. 126168Bytes

distribution Max: 2MBMin: 50Bytes

Number of Truncated Mean: 5.64embedded objects Pareto Max: 53per pages distributionReading time Exponential Mean: 30sec

distributionParsing time Exponential Mean: 0.13sec

distribution

Streaming Video Session Deterministic 3600sec(64kbps) duration(movie)

Inter-arrival time Deterministic 100msbetween the (based onbeginning of 10frameseach frame per secondNumber of packets Deterministic 8 packets per frame(slices) in a framePacket size Truncated Mean: 50Bytes

Pareto Max: 250Bytesdistribution

Inter-arrival time Truncated Mean : 50Bytesbetween the Pareto Max: 12.5mspackets in a frame distribution

Voice VoIP Average call Exponential Mean : 210secholding time distributionVoice CODEC AMR 12.2kbpsFrame length Deterministic 20msTalk spurt length Exponential Mean: 1026ms

distributionSilence length Exponential Mean: 1171ms

distribution

8 Sueng Jae Bae et al.

blocking probability and dropping probability are defined as the ratio of thenumber of rejected new and handoff calls and total number of arrived new andhandoff calls, respectively.

In simulations, CAC tightly coupled with scheduling algorithm is performedonly at the centered cell with Cell 0. CAC in cells except Cell 0 decides acceptanceor rejection of call requests based on new call blocking probability and handoffcall dropping probability measured in Cell 0. While an UE moves from adjacentcells of Cell 0 to Cell 0 during its service time, the generation of the correspondingpackets is started at the handoff-in time and ended at the handoff-out time.If packets are remained in eNB for Cell 0 at the handoff-out time, they arediscarded from the eNB. Since existing CAC algorithms have been developed forchannel-based cellular systems, it is difficult to directly compare the performanceof existing CAC algorithms and the proposed CAC algorithm. Here, we compareperformance of the proposed CAC algorithm with that of the case without CAC.In resource-estimated CAC algorithm, Breq is decided as the service type of therequested call. Based on LTE specification, Breq sets as 8kbps and 20kbps forVoIP and streaming, respectively [18][19]. For web and FTP services, since itis difficult to determine their data rates, the measured data rate of same trafficclass is used as Breq.

Figs. 3 and 4 represent the average data rate and PRB utilization, respec-tively. The maximum average data rates are near by 10Mbps and 7.7Mbps innon-CAC and resource-estimated CAC algorithm, respectively. In addition, max-imum PRB utilizations become 1 and 0.89 for non-CAC and resource-estimatedCAC algorithm, respectively. The proposed CAC algorithm should reject someof requested calls to prevent network congestion, its total average data rate andtotal PRB utilization are less than those in non-CAC.

The average packet delay is shown as Fig. 5. As arrival rate times serviceduration per UE, ρ increases, the average packet delay with non-CAC increases.Since the sizes of packets for non real-time services, i.e., FTP and web, are largerthan those of real-time services, such as streaming and VoIP, the average packetdelays of non real-time services increase more sharply than those of real-timeservices. The average packet delay of resource-estimated CAC algorithm is lowerthan that of non-CAC.

Figs. 6 and 7 illustrate call rejection ratio for real-time and non real-timeservices, respectively. Since packet size of FTP service is larger than those ofother services, the number of rejected calls for FTP service is more than thoseof other services. For resource-estimated CAC algorithm, handoff call droppingprobability is higher than new call blocking probability because MCS level andcode rate for handoff calls at their request time are worse than those for newcalls at their request time.

5 Conclusion

In this paper, to guarantee QoS requirements for packet delay in LTE system,we proposed resource-estimated CAC algorithm. Resource-estimated CAC algo-

Resource-estimated Call Admission Control Algorithm in 3GPP LTE System 9

0.0 0.2 0.4 0.6 0.8 1.010000

100000

1000000

1E7

Ave

rage

dat

a ra

te (b

ps)

arrival rate * service duration per UE

no CAC RECACFTP Web Video VoIP Total

Fig. 3. Average data rate when velocities of all UEs are 4km/h and traffic mixtureratio is 25:25:25:25

0.0 0.2 0.4 0.6 0.8 1.00.0

0.2

0.4

0.6

0.8

1.0

PR

B u

tiliz

tion

arrival rate * service duration per UE

no CAC RECACFTP Web Video VoIP Total

Fig. 4. PRB utilization when velocities of all UEs are 4km/h and traffic mixture ratiois 25:25:25:25

10 Sueng Jae Bae et al.

0.0 0.2 0.4 0.6 0.8 1.01E-4

1E-3

0.01

0.1

1

10

100

1000

Ave

rage

pac

ket d

elay

(sec

)

arrival rate * service duration per UE

no CAC RECACFTP Web Video VoIP

Fig. 5. Average packet delay when velocities of all UEs are 4km/h and traffic mixtureratio is 25:25:25:25

0.0 0.2 0.4 0.6 0.8 1.01E-3

0.01

0.1

1

Cal

l rej

ectio

n ra

tio

arrival rate * service duration per UE

new call handoff callVideoVoIP

Fig. 6. Call rejection ratio of real-time services when velocities of all UEs are 4km/hand traffic mixture ratio is 25:25:25:25

Resource-estimated Call Admission Control Algorithm in 3GPP LTE System 11

0.0 0.2 0.4 0.6 0.8 1.01E-3

0.01

0.1

1

Cal

l rej

ectio

n ra

tio

arrival rate * service duration per UE

new call handoff callFTP Web

Fig. 7. Call rejection ratio of non real-time services when velocities of all UEs are4km/h and traffic mixture ratio is 25:25:25:25

rithm predicts the amount of PRBs required for service requests and it has lowcomplexity. In order to evaluate the performance of the proposed CAC algo-rithm, we performed simulations under various simulation environments. Fromthe simulation results, even though the average data rate and PRB utilization ofproposed CAC algorithm is lower than those of non-CAC, performance of delayof proposed CAC algorithm is better than that of non-CAC. For further studies,research on the enhancement of the proposed algorithm is required for reducinghandoff call dropping probability.

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