TSINGHUA SCIENCE AND TECHNOLOGY ISSN 1007-0214 01/18 pp741-746 Volume 13, Number 6, December 2008

Two-Level Cross-Talked Admission Control Mechanism for QoS Guarantee in 802.11e EDCA

NIU Zhisheng ( )**, LIU Jing ( )

Department of Electronic Engineering, Tsinghua University, Beijing 100084, China

Abstract: This paper describes a two-level cross-talked admission control mechanism that guarantees qual-

ity of service (QoS) requirements for multimedia applications over wireless local area networks (WLANs). An

enhanced distributed channel access analytical model is used to compute the maximum number of admitted

users according to the QoS requirements and the packet arrival characters. Then, some channel resources

are reserved for handoff calls based on the maximum number of admitted users and the call-level traffic

model. The channel utilization ratio is also measured to reflect the current system traffic load. The maximum

number of admitted users and the channel utilization ratio are used for admission control for applications

with QoS requirements in the call level and for rate control of best effort applications in the packet level us-

ing the p -nonacknowledgement scheme. Thus, the QoS requirements are statistically guaranteed while the

system is efficiently utilized. Simulations validate the effectiveness of this mechanism to guarantee the QoS

and bandwidth utilization.

Key words: wireless local area network; enhanced distributed channel access; quality of service; admission

control; rate control

Introduction

IEEE 802.11-based wireless local area networks (WLANs) have been effectively deployed in public and private places because of their low cost, simplicity of installation, and high data rates. Multimedia applica-tions are now rapidly increasing. Multimedia applica-tions such as voice and video will be provided in future WLAN. Unlike traditional data applications, those ap-plications have quality of service (QoS) requirements for the delay, packet loss rate, and other parameters.

The IEEE 802.11 task group specified a distributed access mechanism called enhanced distributed channel access (EDCA) to support service differentiation in the medium access control (MAC) layer by assigning

different access parameters to users of different classes[1]. However, service differentiation cannot guarantee the QoS requirements of each traffic class because of the shared and contention-based nature of the 802.11 channel which was originally designed for best-effort applications. With the increasing number of admitted stations, the collision probabilities also in-crease and the QoS parameters such as packet loss rate and access delay for each station do not satisfy the QoS requirements. Thus, admission control is neces-sary to support applications with QoS requirements. IEEE 802.11e also recommends the use of admission control to guarantee the QoS, but specifies no concrete admission control mechanisms.

Some research has focused on the call admission control mechanisms in 802.11 WLAN[2-7]. Mase[5] proposed a probe flow-based admission control scheme in which the system does the admission deci-sion according to the end-to-end delay detected by the

Received: 2007-03-29; revised: 2008-06-30

** To whom correspondence should be addressed. E-mail: niuzhs@tsinghua.edu.cn; Tel: 86-10-62781423

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probe flow. Zhai et al.[7] proposed a measure-based call admission control scheme in which the access point (AP) detects the channel busy ratio, then does the ad-mission decision. These two schemes make the con-nection admission control (CAC) decision according to the system’s current conditions, but during the call hold time, the condition may change, so the QoS is still not guaranteed. Gao et al.[6]

summarized several model- based CAC mechanisms. These model-based CAC

mechanisms are mainly based on the system through-put rather than the QoS parameters of each user.

This paper describes a two-level admission control mechanism to guarantee the QoS in 802.11e systems under the infrastructure mode. This mechanism first uses a performance analysis model[8] to determine the system capacity for the given QoS requirements. The channel utilization ratio is also monitored as a parame-ter of the system’s current traffic load. Admission deci-sions for QoS applications are performed according to these parameters. The p-nonacknowledgement (p- NACK) mechanism is used for rate control of best ef-fort applications using these parameters.

1 System Analysis

Consider a micro cellular network consisting of ho-mogeneous cells where the behavior of each cell is stochastically identical. Then consider the stochastic behavior of the whole network by considering an arbi-trary cell without loss of generality with the IEEE 802.11e EDCA used for that cell. This cell is assumed to have one AP and works in infrastructure mode without ready-to-send (RTS) and clear-to-send (CTS). Stations in the cell are divided into N classes. The number of stations of class i is denoted by in . Vec-tor 1 2{ ... }Nn n nn is used to denote the user num-bers of all classes.

1.1 Analysis for maximum admitted users

EDCA lacks the ability to allocate specific bandwidth for users with heterogeneous QoS requirements. The delay and packet losses are two important parameters to define the QoS. Call admission control is used to guarantee that the current admitted user will not de-grade the QoS of existing users. To achieve this goal, define { }n where for each vector n in , the system performance satisfies

( )

( )i

i i

i m

l m

P d D

P p P (1)

where di denotes the delay and il

p the packet loss

rate of a class i packet. imP denotes the maximum

packet loss rate and imD the maximum access delay

of a class i packet based on the QoS requirements. Thus, denotes the system capacity for a set of

QoS requirements. If n is in , then the delay and packet loss requirements are guaranteed. The edge points of denote the maximum number of admit-ted users for the conditions. Liu and Niu[8] gave an analytical model to predict the maximum number of users for a set of QoS parameters and specified system conditions. Thus, can be related to specific QoS requirements.

For the performance analysis model, consider a sta-tion of access class i . Packets queue in the sender buffer with the first in first out (FIFO) order so the buffer can be modeled as a queueing system. The sys-tem’s arrival process defines this station’s packet gen-eration process, while the service time is the sum of the back-off delay and the transmit delay. Experiments and analyses have shown that the service time can be ap-proximated as an exponential distribution with an av-erage of 1

i[7]. Thus, the access process of a class i

station packet is a GI/M/1 queueing model, so the whole access delay can be calculated. The average backoff delay, id , is required to determine 1

i . A two-dimensional Markov process was used to analyze the backoff and calculate the transmission probabil-ity, i. Then id and

ilp were obtained, CW 1

0 0 0{ } { }

li k

i k il iltl k t

d P C l P B k d (2)

1i

i

Rl ip p (3)

where kC denotes the number of collisions that an AC i station experiences, ilB denotes the backoff counter of a class i station that has l collisions, CW is the current class i contention window, iR is the maximum number of retries, ip denotes the collision probability when a class i packet is trans-mitted and iltd denotes the average time duration of the time between the t 1 and t backoff counter decrements of a class i packet after the l-th collision.

NIU Zhisheng ( ) et al Two-Level Cross-Talked Admission Control Mechanism for QoS … 743

ip and iltd are calculated from i , which is calcu-lated from n .

After calculating based on the resources for each class, then 1 2{ }Nn n nn in is used for the admission decisions. To fully utilize the WLAN bandwidth, n is always maximized within to achieve higher system throughput.

1.2 Handoff and traffic model

Once the number, in , of admitted users in class i is determined, the system for class i users can be treated as a service system with a fixed number of channels as shown in Fig. 1. Each user’s QoS require-ment is guaranteed on the call level by reserving a spe-cific number of channel for handoff users. The number of reserved class i channels is denoted as

irn . This is

calculated using queueing theory to guarantee that the handoff call dropping probability is less than .

Fig. 1 Service system model for class i users

irn is calculated based on a call-level traffic model

for the fixed number of channels service system. In this model, calls being serviced in a cell release their resources if the calls are finished in the current cell or if the mobile terminal moves into a neighboring cell with the calls unfinished. The time for which station i dwells in the current cell is defined as

iwT and the

total call duration during which the call is not termi-nated is defined as

icT . Assume that iwT and

icT are

exponentially distributed and independent of each other. The average value of

iwT is 1iw and of

icT is 1

ic . Thus, the channel holding time for call ihT is

also exponentially distributed with a mean of 1ih

where i i ih w c . Assuming a uniform density of

terminals throughout the area and arbitrary movement directions with respect to the cell boundary,

iw can

be given by ( )

iwE v L

A (4)

where ( )E v is the average speed of terminals in the cell, L is the cell perimeter, and A is the cell area.

For handoffs, assume that the arrivals of new calls are Poisson-distributed with rates

in , which denotes

the arrival rate of handoff calls. The mean number of incoming users into a cell based on an equilibrium homogeneous mobility pattern is equal to the number of outgoing users from the cell. Thus,

( )i ih i wE n (5)

where ( )iE n is the average number of class i sta-tions. These service parameters of arrival handoff calls can be used to calculate

irn for each class i .

1.3 Channel utilization ratio

The maximum number of admitted users in each call level was derived in Section 1.1. In practice, the access channel load is time varying. The channel utilization ratio is used to show the channel’s short time scale traffic load[7]. The channel utilization ratio, bR , is the ratio of the time the channel is busy to the total time. The busy time includes both successful transmissions and collisions. This ratio is calculated by the hardware of current 802.11 network adapter cards. bR can then be calculated using

c c s s AIFS c sb

c c s s e

( )p T p T T p pRp T p T p

(6)

where sp , cp , and ep denote the probabilities that a slot will experience a successful transmission, a colli-sion, or be empty. AIFST is the time of the arbitrary inter-frame space. sT , cT , and denote the average times for a successful transmission, a collision, or be-ing empty.

Figure 2 illustrates the average backoff delay as a function of the channel busy ratio predicted by the analytical model for data and voice applications. The channel busy ratio increases with increasing number of stations. For channel busy ratios below 0.8, the data and voice delays both increase very slowly with in-creasing channel busy ratio. However, when the chan-nel busy ratio is between 0.8 and 0.9, the delay in-creases fast but can still support some realtime applica-tions. When the channel busy ratio is greater than 0.9,

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the delay increases very rapidly and cannot satisfy the QoS requirements. Thus, the maximum system throughput is achieved for bR between 0.8 and 0.9, while for bR greater than 0.9, the system becomes saturated. Thus, rate control and admission control for newly arrived users should be based on channel busy ratios between 0.8 and 0.9 to guarantee the QoS re-quirements of admitted users.

Fig. 2 Predicted backoff delays for data and voice applications

2 Admission Control Mechanism

The dynamic two-level admission control mechanism uses call admission control in the call-level timescale and rate control in the packet-level timescale. The us-ers of higher priority classes with QoS requirements are referred to as QoS users. The call-level admission control prevents the admission of excessive QoS users that will reduce the QoS of existing QoS users. The packet-level rate control prevents the best effort users from reducing the QoS of the QoS users. The admis-sion control scheme is implemented at the QoS APs of 802.11e, which collect traffic and channel information. Before setting up a new call with QoS requirements, each station first transmits an add traffic stream (ADDTS) request message to the AP. For handoffs, an ADDTS request is also sent to the new AP. The ADDTS request message contains the traffic stream class information. Upon receiving a new ADDTS, the AP matches the class information of the new station to class i .

The two-level admission control algorithm then de-cides whether to accept an incoming class i call us-ing the algorithm shown in Fig. 3.

Fig. 3 Admission control algorithm

When receiving an ADDTS, the algorithm first evaluates the class i conditions. If the new call be-longs to the lowest priority best effort application, it is accepted. Then the rate control is applied to the call in the packet-level timescale. If the call is a QoS user, for handoff QoS user, the algorithm only evaluates metric

bR for the CAC decision. For new QoS users, bR and in are both evaluated for the CAC decision where

in represents the average long time system load and

bR represents the system traffic load on a much shorter timescale. A threshold, iR , is chosen to identify unac-ceptable conditions worse than in . Thus, handoff users have higher priorities than new users. Users of differ-ent classes also have different iR which results in dif-ferent priorities.

Rate control in the packet level is only used for best effort applications, where the best effort is class 0 . If

b 0R R , the QoS AP does not send an ACK to the best

effort user with probability 0

0

1 nn

. Thus, the effective

bandwidth for 0n best effort users is equal to the sum of the required bandwidth of 0n users. This p-NACK rate control mechanism enlarges the backoff window of best effort users if the system traffic load is too heavy. Thus, the effective bandwidth of best effort us-ers is restricted to control their effect on the QoS users.

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3 Simulation Results

Consider a single cell providing combined voice over IP (VoIP) and data services. A practical voice traffic model is presented as an example using the conversa-tion model specified in the ITU P.59 recommenda-tion[9]. The voice and data traffic and packet parame-ters are summarized in Table 1.

Table 1 Traffic parameters

Parameters Value Unit Packets interval Ti (voice) 20 ms Packet size (voice) 40 Bytes Mean packets size (data) 500 Bytes Average interval (data) 100 ms

1ic (voice) 120 s ( )E v 0.2 m/s

L 100 m 0.01

The VoIP QoS requirements include a maximum de-

lay and packet loss rate. Assume that the end-to-end voice delay is to be below 150 ms and the packet loss rate is to be below 3%[10]. The network queueing delay can be reduced to about 50 ms, the codec and packet process delay are about 20 ms in total, and the receive jitter buffer delay is on average 60 ms. Then assume that the average access delay is less than 20 ms and the average back-off delay is less than 10 ms, so the 99% back-off delay is less than 30 ms.

Simulations based on these parameters then show the performance of the CAC mechanism. The voice and data station arrival rates,

in, were gradually in-

creased to simulate an increasing system traffic load with the simulation results in Figs. 4-6 showing the CAC performance for various traffic loads. 1n for the voice user was set to 20, the chosen

1rn was from 1 to

2 as a function of the user arrival rate. The call drop rate during the simulations was 0.6.

The results in Figs. 4 and 5 show that the CAC mechanism statistically guarantees the QoS of the real-time VoIP stations. The algorithm keeps the back-off delay of the VoIP stations always below 10 ms with a packet loss rate below 2%. At the same time, the de-lay and packet losses of the data stations without the QoS requirements gradually increase with the traffic load. The results in Fig. 6 compare the two-level CAC

Fig. 4 Voice and data delays

Fig. 5 Voice and data packet loss rates

Fig. 6 System throughput

mechanism with a mechanism using only call-level admission control. With the single-level CAC mecha-nism, as the traffic load increases, the number of ad-mitted voice users decreases to maintain the QoS of the voice users. With the two-level mechanism, the rate control prevents data load’s effect on the voice users, so the total throughput of all voice users fluctuates slightly around a fixed number determined by in . The results also show that the data users utilize remained

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bandwidth resource by using our mechanism, so the whole throughput of all users also keeps fluctuating around some number near the system’s maximum throughput.

4 Conclusions

A two-level cross-talked admission control mechanism was developed to guarantee the QoS requirements of realtime applications. This mechanism need not mod-ify the existing MAC protocol. The mechanism uses admission control in both the call level and the packet level. The call-level admission control prevents new QoS users from reducing the QoS of existing QoS us-ers. The packet-level rate control prevents the best ef-fort users from reducing the QoS of QoS users. These admission control schemes interact and share informa-tion with each other, including packet arrival charac-teristics, traffic levels, and the channel utilizaion ratio. Thus, the QoS requirements of realtime multimedia users can be guaranteed while efficiently using the system bandwidth. Simulations show that this admis-sion control scheme can guarantee the QoS parameters of VoIP applications such as access delay and packet loss rate while the best effort data users utilize the re-maining bandwidth. Meanwhile, reserved resources guarantee call drop rates of the handoff users and the rate control scheme guarantees that increased number of data users number have no impact on the QoS of the VoIP users.

References

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