A Resource-estimated Call Admission Control ... - Semantic Scholar

A Resource-estimated Call Admission Control Algorithm in 3GPP LTE System ⋆ Sueng Jae Bae1 , Jin Ju Lee1 , Bum-Gon Choi1 , Sungoh Kwon2 , and Min Young Chung1⋆⋆ 1

School of Information and Communication Engineering Sungkyunkwan University 300, Chunchun-dong, Jangan-gu, Suwon, Kyunggi-do, 440-746, Korea 2 Telecommunication R&D Center SAMSUNG ELECTRONICS 416, Maetan-dong, Youngtong-gu, Suwon, Kyunggi-do, 443-742, Korea

Abstract. As the evolution of high speed downlink packet access (HSDPA), long-term evolution (LTE) has being standardized by the 3rd generation partnership project (3GPP). In the existing mobile communication networks, voice traffic is delivered through circuit-switched networks, but to the contrary in LTE, all kinds of traffic are transferred through packet-switched networks based on IP. In order to provide quality of service (QoS) in wireless networks, radio resource management (RRM) is very important. To reduce network congestion and guarantee certain level of QoS for on-going calls, call admission control (CAC), in part of RRM, accepts or rejects service requests. In this paper, we proposed resource-estimated CAC algorithm and evaluated the performance of the proposed CAC algorithm. The result shows that the proposed algorithm can maximize PRB utilization and guarantee certain level of QoS. Key words: LTE System, CAC, QoS, RRM

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Introduction

LTE has being standardized by 3GPP as part of 3GPP release 8 [1]. By adopting orthogonal frequency division multiple access (OFDMA) and multiple-input multiple-output (MIMO) technologies, LTE increases data rate and improves spectral efficiency [2]. In addition, since LTE evolves from HSDPA, it can be easily compatible with the current mobile communication networks [3]. In the existing mobile communication networks, voice traffic is delivered through circuitswitched 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 Ministry of Knowledge Economy), Korea, under the ITRC(Information Technology Research Center) support program supervised by the IITA(Institute for Information Technology Advancement) (IITA-2009-C1090-0902-0005). Dr. M.Y. Chung is the corresponding author.

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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 certain level of QoS for on-going calls, CAC, in part of RRM, decides acceptance or rejection of service requests. Evolved universal terrestrial radio access network node B (eNB), base station in LTE system, may perform CAC as several conditions, such as channel status, QoS requirements for requested services, buffer state in eNB, and so on [1][5]. The existing CAC algorithms can be classified into two categories, static and dynamic. 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 radio channel state and available resources [11][12][13]. Since dynamic CAC algorithms assume that all requested calls have the same QoS requirement, they can not directly adapt to LTE system which provides various kinds of services. In this paper, we propose a resource-estimated CAC algorithm. Whenever a service request occurs, the resource-estimated CAC algorithm estimates the number of Physical Resource Blocks (PRBs) required for the service request. Based on the service type and modulation and coding scheme (MCS) level of user,the number of required PRBs is determined. Since the resource-estimated CAC algorithm considers minimum data rate required for the requested service, it can maximize the utilization of physical resources. We conduct intensive simulation in order to evaluate performance of the proposed CAC algorithm. The rest part of this paper is organized as follows. Section 2 describes existing CAC algorithms. The proposed CAC algorithm is discussed in Section 3. In Section 4, we analyze the performance of proposed algorithm. Finally, conclusions are presented in Section 5.

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Pre-studied CAC Algorithms

The existing CAC algorithms can be divided into static and dynamic. Static CAC algorithms reserve resources for handoff calls [6][7][8][9]. Dynamic CAC algorithms perform admission control through estimation of radio channel status and available resources [11][12][13]. In this section, we explain existing three static CAC algorithms, guard channel, fractional guard channel, and queueing principle. In addition, we illustrate existing three dynamic CAC algorithms, local predictive, distributed, and shadow cluster. 2.1

Static CAC algorithms

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

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

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users. Moreover, the guard channel algorithm may decrease utilization of physical resources 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 channel algorithm determines an acceptance or a rejection of new calls with decision probability varied as the number of busy channels. In case that channels are sufficiently available, handoff calls are accepted, but new calls can be rejected. Thus, in the fractional guard channel algorithm, dropping probability of handoff calls may be smaller than that of new calls. In addition, since decision probability varies as the number of available channels, the fractional guard channel algorithm alleviates congestion in the network. However, channel reservation schemes, such as guard channel and fractional guard channel, may use inefficiently wireless resources [10]. Moreover, channel reservation schemes may excessively block new calls compared with handoff calls, because they always reserve some channels for handoff calls [14]. To overcome disadvantages of channel reservation schemes, queueing principle algorithms were proposed [8][9]. In these algorithms, call requests are accepted when there exist available channels. However, if all the channels are unavailable, new and handoff calls are registered in the waiting list as their queueing discipline. When channels go into idle state due to call release or handoff, they are 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]. The local predictive CAC algorithm estimates the amount of resources required for a serving call based on Wiener processes. In addition, it predicts arrival times for handoff calls and then reserves resources for the handoff calls. The local predictive CAC algorithm has lower dropping probability for handoff calls than that for new calls because of adaptively preserving wireless resources for handoff calls. However, to correctly predict arrival times and required bandwidth of handoff calls, information on handoff calls should be shared with base stations related with the handoff calls. Naghshineh et al. proposed distributed CAC algorithm which predicts local resources as well as resources of adjacent cells [12]. The distributed CAC algorithm considers the number of handoff calls moving from adjacent cells and their QoS requirements. The distributed CAC algorithm guarantees the QoS of handoff calls more effectively than the local predictive, because neighbor base stations exchange information on handoff calls, such as arrival rate, required bandwidth, etc. However, the distributed CAC algorithm assumes that all calls have the same service type and QoS requirement. In multimedia wireless networks, there exist 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, performs admission control considering movement of UEs, i.e., velocity, movement direction, and position in cell of UEs [13]. According to movement of user equipment

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

3

Proposed CAC algorithm

Resource-estimated CAC algorithm estimates the number of PRBs which should be allocated to the requested call. The number of required PRB should be decided by reflecting the type of the requested service and current MCS level of UE. In addition, the resource-estimated CAC algorithm calculates available PRBs based on PRB usage of on-going call measured by eNB. Fig. 1 illustrates the flow chart of call admission control procedure in the resource-estimated CAC algorithm. Admission request

Estimate resources for requested call and available resources PRB N req =

Breq PRB BMCS

,

PRB PRB PRB N PRB free = N total − N RT − N NRT

PRB N PRB ? free > N req

No

Reject call

Yes

Accept call

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

P RB P RB In Fig. 1, Nreq , Breq , and BM CS denote the number of required PRBs per one second, required data rate, and the number of bits carried in a PRB under the current MCS level of an UE requesting a service, respectively. Since transmission P RB data rate in wireless environment varies as channel condition, Nreq is calculated P RB as Breq over BM CS . Breq is determined as the service type of the requested call. P RB BM CS is calculated as channel quality indicator (CQI) information reported from the corresponding UE through physical uplink control channel (PUCCH) or physical uplink shard channel (PUSCH). In general, handoff call requests occur


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when corresponding UEs cross over cell boundary. Thus, for handoff calls, we P RB P RB use the smallest BM CS among possible BM CS s under the given cell environment. P RB Nf ree denotes the total number of available PRBs during the past one second. RB To find NfPree , eNB calculates PRB usage of on-going calls at arrival time of a P RB P RB service request. NRT and NN RT denote the number of PRBs during the past one second that eNB actually allocates to real-time services and non real-time P RB services, respectively. The total number of PRBs per second, Ntotal is decided P RB as the channel bandwidth of LTE system. Thus, Nf ree is easily obtained by P RB P RB P RB P RB subtracting the sum of NRT and NN RT from Ntotal . If Nf ree is bigger than P RB Nreq , the requested call is accepted. Otherwise, the requested call is rejected. Since resource-estimated CAC algorithm only estimates minimum data rate of the requested service, it can be easily implemented.

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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 radio access network consisting of seven hexagonal cells. Radius of each cell is 250m and identification numbers of cells are 0 to 6, as shown in Fig. 2. In addition, we assume proportional fair (PF) scheduling scheme as MAC scheduling algorithm [17]. We consider an OFDMA system with 5 MHz of downlink channel bandwidth which is one of the channel bandwidths specified in LTE system. For wireless channel conditions, path-loss and multi-path fading are considered but inter-cell interference is not reflected on our simulation. To determine the MCS level of an UE, we use modified COST 231 Hata model which reflects 10 dB log-normal shadow fading[15]. The number of UEs is 1750 and their positions are uniformly distributed in seven cells at the starting time of simulation. The mobility model is considered as random-walk model. The velocities of all UEs are assumed to be 4km/h, and flight time of all UEs is uniformly distributed between 10 sec and 20 sec. Service requests arrive at eNB as Poisson processes with parameter λ 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 0 sec to 2,000 sec is ignored. eNB has a logical queue per service of an UE with 10MBytes. 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 service types. The traffic mixture ratio of FTP, web, video, and VoIP is considered 25:25:25:25, 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 of handoff calls are considered. The average data rate is defined as the ratio of total 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 transmission time and mean queue-waiting time. PRB utilization is defined as the ratio of the number of PRBs allocated to UEs during the whole simulation time. The

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Cell 1

Cell 2

Cell 6

Cell 0

Cell 3

Cell 5

Cell 4

Fig. 2. Cell structure in simulation

Table 1. The simulation parameters Parameter Number of cells Radius of cell Number of UEs Velocity of UEs Flight time of UEs Downlink channel bandwidth Traffic mixture ratio Simulation time Service duration Queue length TTI

Value 7 250m 1750 4km/h [10, 20] sec 5MHz 25:25:25:25 10,000 sec 180sec 10MB 1ms

Note hexagonal shape

uniform distribution 50 PRBs per TTI FTP:web:video:VoIP 0–2,000 sec is ignored exponential distribution


Table 2. Characteristics of traffic considered for simulation QoS class

Service

Best effort FTP

Component File size

Interactive Web Number of data browsing pages per session (HTTP) Main object size

Statistical Characteristics Truncated log normal distribution Log normal distribution Truncated log normal distribution

Embedded object Truncated size log normal distribution Number of embedded objects per pages Reading time Parsing time

Truncated Pareto distribution Exponential distribution Exponential distribution Deterministic

Streaming Video Session (64kbps) duration(movie) Inter-arrival time Deterministic between the beginning of each frame Number of packets Deterministic (slices) in a frame Packet size Truncated Pareto distribution Inter-arrival time Truncated between the Pareto packets in a frame distribution Voice VoIP Average call Exponential holding time distribution Voice CODEC AMR Frame length Deterministic Talk spurt length Exponential distribution Silence length Exponential distribution

Parameters Mean: 2MB Std.dev.: 0.722MB Max: 5MB Mean : 17 Std.dev.: 22 Mean: 10710Bytes Std.dev.: 25032Bytes Max: 2MB Min: 100Bytes Mean: 7758Bytes Std.dev. 126168Bytes Max: 2MB Min: 50Bytes Mean: 5.64 Max: 53 Mean: 30sec Mean: 0.13sec 3600sec 100ms (based on 10frames per second 8 packets per frame Mean: 50Bytes Max: 250Bytes Mean : 50Bytes Max: 12.5ms Mean : 210sec 12.2kbps 20ms Mean: 1026ms Mean: 1171ms

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blocking probability and dropping probability are defined as the ratio of the number of rejected new and handoff calls and total number of arrived new and handoff calls, respectively. In simulations, CAC tightly coupled with scheduling algorithm is performed only at the centered cell with Cell 0. CAC in cells except Cell 0 decides acceptance or rejection of call requests based on new call blocking probability and handoff call dropping probability measured in Cell 0. While an UE moves from adjacent cells of Cell 0 to Cell 0 during its service time, the generation of the corresponding packets 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 are discarded from the eNB. Since existing CAC algorithms have been developed for channel-based cellular systems, it is difficult to directly compare the performance of existing CAC algorithms and the proposed CAC algorithm. Here, we compare performance 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 the requested call. Based on LTE specification, Breq sets as 8kbps and 20kbps for VoIP and streaming, respectively [18][19]. For web and FTP services, since it is difficult to determine their data rates, the measured data rate of same traffic class is used as Breq . Figs. 3 and 4 represent the average data rate and PRB utilization, respectively. The maximum average data rates are near by 10Mbps and 7.7Mbps in non-CAC and resource-estimated CAC algorithm, respectively. In addition, maximum PRB utilizations become 1 and 0.89 for non-CAC and resource-estimated CAC algorithm, respectively. The proposed CAC algorithm should reject some of requested calls to prevent network congestion, its total average data rate and total PRB utilization are less than those in non-CAC. The average packet delay is shown as Fig. 5. As arrival rate times service duration 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 larger than those of real-time services, such as streaming and VoIP, the average packet delays of non real-time services increase more sharply than those of real-time services. The average packet delay of resource-estimated CAC algorithm is lower than that of non-CAC. Figs. 6 and 7 illustrate call rejection ratio for real-time and non real-time services, respectively. Since packet size of FTP service is larger than those of other services, the number of rejected calls for FTP service is more than those of other services. For resource-estimated CAC algorithm, handoff call dropping probability is higher than new call blocking probability because MCS level and code rate for handoff calls at their request time are worse than those for new calls at their request time.

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Conclusion

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


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Average data rate (bps)

1E7

1000000

100000

no CAC RECAC FTP W eb Video VoIP Total

10000 0.0

0.2

0.4

0.6

0.8

1.0

arrival rate * service duration per UE

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

no CAC RECAC

1.0 FTP W eb Video

PRB utiliztion

0.8

VoIP Total

0.6

0.4

0.2

0.0 0.0

0.2

0.4

0.6

0.8

1.0


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

10


1000

no CAC RECAC FTP W eb

100

Video

Average packet delay (sec)

VoIP

10

1

0.1

0.01

1E-3

1E-4 0.0

0.2

0.4

0.6

0.8

1.0


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

Call rejection ratio

1

0.1

0.01

new call handoff call Video VoIP

1E-3 0.0

0.2

0.4

0.6

0.8

1.0


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


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Call rejection ratio

1

0.1

0.01

new call handoff call FTP W eb

1E-3 0.0

0.2

0.4

0.6

0.8

1.0


Fig. 7. Call rejection ratio of non real-time services when velocities of all UEs are 4km/h and traffic mixture ratio is 25:25:25:25

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

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