Call Admission Control for IEEE 82.11 Contention Access Mechanism Dennis Pong and Tim Moors School of Electrical Engineering and Telecommunications, The University of New South Wales, Australia Email: dennisp@student.unsw.edu.au and t.moors@unsw.edu.au Abstract This paper proposes an admission control algorithm that enables the upcoming IEEE 82.11e contention based Enhanced Distributed Channel Access (EDCA) to provide quantitative bandwidth guarantees for Wireless Local Area Networks (WLANs), rather than a relative prioritized service. The algorithm estimates the throughput that flows would achieve if a new flow with certain parameters was admitted, and so indicates whether such a new flow can be admitted while preserving the Quality of Service (QoS) of existing flows. The algorithm deals with the EDCA parameters of minimum contention window size and transmission opportunity duration, and indicates what values should be used for different flows. Simulation results confirm the accuracy of the throughput estimates and the effectiveness of the admission control algorithm. I. INTRODUCTION The IEEE 82.11 MAC uses the contention based Distributed Coordination Function (DCF) as the basic access mechanism and a polling based Point Coordination Function (PCF) to provide contention free channel access. IEEE 82.11 task group E is defining mechanisms to enhance the QoS of the original MAC standard, including a new contention access scheme called Enhanced Distributed Channel Access (EDCA) [1] and an improved polling scheme based on PCF. Contention based access mechanisms are simple and robust, but they cannot guarantee delay and throughput because of their probabilistic nature. With small modification to the DCF access mechanism, which virtually all wireless LAN cards implement, relative priority service is provided. This paper focuses on the contention based EDCA access scheme, which provides a prioritized QoS service using an independent transmit queue and channel access function for each traffic class. Traffic belonging to a higher priority class has a higher probability of transmission, thus achieving a higher throughput when competing with lower priority traffic. However, no assurance can be given to higher priority traffic in terms of throughput and delay performance. The problem is especially apparent when the wireless channel is overloaded causing the bandwidth share of each flow to diminish. This limits the use of EDCA for many multimedia applications that are not capable of dynamically adapting to the available bandwidth at any instant. Since they require absolute bandwidth provision from the networks, resource reservation and management becomes a significant issue in WLANs especially when bandwidth is relatively scarce and has to be shared by many users. Admission control is an important tool to maintain QoS experienced by users. In this paper, we propose an admission control algorithm for the 82.11e EDCA that will take account of dynamic network conditions such as the number of active flows and the parameters adopted for these data flows. By predicting the achievable throughput of the data flows and avoiding channel overloading, the QoS of existing flows can be maintained. We incorporate the concepts of EDCA into the admission control algorithm and extend its ability to provide bandwidth guarantees, instead of providing a relative prioritized service. Simulation results demonstrate that by implementing the proposed admission algorithm, the throughput of existing flows can be protected and resources can be managed effectively according to network conditions. The algorithm is able to select suitable parameters for good bandwidth and delay performance. II. NEW FEATURES IN 82.11E EDCA The EDCA access mechanism supports relative priority service through the introduction of Access Categories (ACs). Instead of using a single queue and one channel access function as in DCF, each station implements multiple ACs. Each AC consists of an independent transmit queue and a channel access function with its own parameters, that include minimum and maximum Contention Windows (CWmin, CWmax), Arbitration Interframe Space (AIFS) and Transmission Opportunity (TXOP) duration. Operation of each channel access function is similar to DCF. Data transmission begins when the medium is idle for more than the AIFS time, with AIFS DIFS (DCF Interframe Space, see Fig. 1). If the medium is determined to be busy, the access function sets its backoff timer to an initial value of random(,cwmin[ac]) slot time. The backoff timer is decremented by a slot time for each time slot after the medium is sensed idle for an AIFS time and stops decrementing when a transmission is detected. It resumes when the medium is sensed idle again for an AIFS time. A channel access function can begin transmission on the channel as soon as the backoff timer reaches zero. An acknowledgement frame is sent to the sender to indicate a successful transmission of a data frame. If no acknowledgement is received after a Short IFS (SIFS), a collision is presumed to have occurred. The backoff timer after a collision is chosen to be random(,(cwmin[ac]+1) 2 i -1) slot time for each retransmission attempt i. In other words, the contention window size is doubled for each retransmission to reduce the probability of collision. Higher priority ACs adopt lower values for CWmin and AIFS to yield a higher
probability of successfully contending for channel access. TXOP). Other factors such as channel interference, signal strength and multipath fading will not be considered here since they cannot be known a priori. 4 35 3 medium priority, increasing bit rate Fig. 1: Backoff timer, IFS and CW in EDCA access Throughput (bytes/sec) 25 2 15 highest priority Another new feature in 82.11e is the TXOP which is the period a channel access function has the right to access the medium after a successful contention, with the maximum duration defined in TXOPLimit[AC]. An EDCA-TXOP is obtained through contention channel access. A channel access function is allowed to continue transmission after waiting a SIFS following the successful completion of a frame exchange sequence, as long as the total transmission time does not exceed TXOPLimit[AC]. III. MAINTAINING QOS IN EDCA Real time multimedia traffic generated by applications like video conferencing, video on demand and voice over IP requires certain level of QoS. In a WLAN, it is crucial to restrict the volume of traffic in order to maintain service quality of current serving traffic. If there are no restrictions to limit the volume of traffic being introduced to the service set, throughput degradation and high medium access delay will result due to increasing backoff time. This is undesirable for inelastic traffic such as video and voice applications that requires a certain level of bandwidth and delay guarantees. In EDCA there is the problem of the spill over effect when traffic is overloaded in an AC, performance in other ACs will also be affected. Unlike wired networks where bandwidth provision can be managed at a centralized point, usually located at the boundary of a subnetwork, transmission queues in a WLAN are distributed and share the same medium. The extent of the effect will depend on the parameters adopted for the ACs and the current network condition. This problem is highlighted in Fig. 2 which shows a simulation of 8 Constant Bit Rate (CBR) streams at 1.5Mbps - 4 streams each in both (lowest priority) and (highest priority). Two streams in start transmitting at 1.5Mbps and increase their bit rate by 4kbps every 5 seconds. Without admission control, the channel becomes overloaded after 13 seconds. The effect on throughput performance of and is different due to different parameters being selected (see section V). By limiting the total admitted traffic in the WLAN below the total achievable throughput, current admitted traffic can be protected and channel utilization will not degrade significantly. The difficulty of implementing this approach in 82.11 lies in estimating the value of the achievable throughput in the WLAN. This value depends on several time varying factors including the number of active stations, the offered traffic volume and each AC s parameters (i.e. CWmin, CWmax, 1 5 lowest priority 5 1 15 2 25 3 35 4 45 5 Time (sec) Fig. 2: Effect of channel overloading Due to the non-deterministic nature of packet arrivals and the random nature of backoff timer values, it is difficult to provide absolute guarantees on delay and throughput on a small time scale. However, it is possible to derive bounds on achievable throughput over the longer term as channel statistics are collected and averaged. The achievable throughput can be estimated with minimal signaling cost and complexity. We argue that by keeping the traffic flow below this throughput limit, QoS can be maintained. Video and voice traffic, that generally have long (>6 seconds) connection times with similar packet statistics for each connection, will benefit from this approach. Admission control will help to maintain good user perceived quality by avoiding channel overloading. IV. ADMISSION CONTROL FOR EDCA A. Estimation of achievable throughput This section introduces a flow based throughput estimation algorithm for EDCA. A flow, in the context of this discussion, is defined as a set of packets belonging to the same AC of a station and uses the same set of parameters and backoff timer. The algorithm utilizes collision statistics of each flow to predict the achievable throughput. The estimation of achievable throughput for each data flow is based on the 82.11 MAC analytical model proposed in [2]. We extend this work to estimate the throughput of flows, each with different channel access parameters and incorporate the concept of TXOP into the algorithm. In this model, the transmission probability of a flow can be derived with the following formula: P(tx in a slot flow = i) = 2(1 2p i ) (1 2p i )(W +1)+p i W (1 (2p i ) b ) p i = long term collision probability of flow i W = CW min size used for flow i b = maximum backoff stage with CW max =(CW min +1) 2 b 1 The model assumes that the packet collision probability is constant and independent of the transmission state. Traffic sources are assumed to have unlimited data to send. Since we are primarily interested in predicting achievable throughput (1)
P(successful transmission flow = i) Data payload size Achievable throughput[i] = (2) P(collision) Duration collision + P(slot is idle) aslott ime + P(successful transmission) Duration success Access scheme Cycle type Frame sequence in the cycle Basic Successful data tx Data frame + SIFS + ACK frame + DIFS Data frame + DIFS RTS/CTS Successful data tx RTS + SIFS + CTS + SIFS + Dataframe + SIFS + ACKframe + DIFS RTS + DIFS RTS/CTS with TXOP Successful data tx RTS + SIFS + CTS + SIFS + TXOPLimit[AC]+ DIFS RTS + DIFS Table 1: Cycle duration of different access schemes for each traffic flow under saturation conditions, the stated assumptions will not have a great impact on the accuracy of the results. The algorithm uses parameters decided at run-time including CWmin, CWmax, physical layer transmission rate and TXOP duration. Given these parameters and the monitored collision rate of each flow, the transmission probability for flow i at saturation condition is calculated using (1). In order to estimate the saturation throughput, three pieces of information are derived - the probabilities of transmission, collision and idle in a slot. The probability of a successful transmission with flow i is given by the product of the probability of flow i transmitting and all other flows not transmitting. A collision occurs when multiple flows transmit in the same slot with the probability given by the difference between probability of one or more transmissions in a slot and only one transmission in a slot. The slot is idle when all stations are not transmitting. Assuming that there are m active flows, the probabilities are calculated as follows: P(successful tx flow = i) = P(tx in a slot flow = i) (1 - P(tx in a slot flow i)) P(successful tx in a slot) = m P(successful tx flow = i) P(1 or more tx in a slot) = 1 - m (1 - P(tx in a slot flow = i)) P(collision in a slot) = P(1 or more tx in a slot) - m P(successful tx in a slot flow = i) P(slot is idle) = 1 - P(1 or more tx in a slot) With these probabilities, the achievable throughput of flow i is the proportion of time for transmitting data payload in respect to idle, collision and header transmission time, during a cycle of frame exchange. A cycle of frame exchange consists of several collision cycles and one successful data frame transmission plus header transmission and idle times. The achievable throughput for flow i is calculated as in equation (2). The cycle duration time is the time required to transmit the frame sequence plus the associated preambles and physical layer headers in the cycle (please refer to table 1). B. Implementation of Admission Control The proposed admission control uses the above achievable bandwidth estimation algorithm. The main objective is to prevent channel overload and protect admitted flows. This is particularly important to inelastic multimedia traffic that are sensitive to bandwidth fluctuations. The data stream will not be admitted if the control algorithm determines that there is insufficient bandwidth to service the new stream. The algorithm also aids in the selection of parameters such as contention window size and transmission opportunity duration, as finding the right parameters can be difficult [3]. The admission control is implemented in the Access Point (AP) operating in infrastructure mode with the responsibility of collision monitoring, throughput estimation and making admission decisions. Each active flow needs a counter to keep track of the collision rate. The collision rate is calculated every update period using exponential weighted average according to (3) to remove short term fluctuations due to interference. We adopted a value of.8 for α and 1 second for update period in our simulation. These two parameters control the variability of the estimated throughput. We believe the values chosen provide a good balance between removing short term fluctuations and reflecting the long term trend. Further optimization may be needed to determine the best parameters according to traffic behaviour. p i,new average =(1 α)p i,current + αp i,previous average (3) From this information, the admission controller is able to predict the achievable throughput of the flows. Stations need to make a request to the admission controller to obtain the desired bandwidth before transmission. If admitting the new flow causes total admitted traffic to exceed the limit, the request is rejected. The admission algorithm searches for the best parameters for CWmin and TXOP duration (if used), given the required bandwidth of the new stream. When a new flow request is made, the collision rate is initialized to the average collision rate of the existing flow having a similar achievable throughput as the desired bandwidth of the new flow. The basic operation involves iteratively reducing the contention window size and estimating the achievable throughput until it matches the bandwidth description of the new flow. Alternatively, if TXOP is used, the TXOP duration can be increased to yield a higher achievable throughput for the flow. for each update interval { if(no new flow requests) { update parameters and collision prob in record; run achievable bandwidth estimation algorithm; else if(new flow request exists) { collision rate[m+1] = collision rate of flow with similar throughput; for i = 1:num active flows + 1
calculate achievable throughput[i]; fori = 1:numactive flows + 1 { if(request bw[i] > achievable throughput[i]) { reduce CW or increase TXOP; re-estimate achievable throughput; if(cw or TXOP limit is reached) { reject new flow; restore to original parameters; exit program; admit new flow; distribute new parameters to stations; Fig. 3: Pseudocode of admission control V. SIMULATION RESULTS Simulations were conducted using the network simulator (ns-2) [4]. In these simulations, no hidden stations were present and the channel was assumed to be error free. All stations operated at 36Mbps complying with the 82.11a physical layer standard and had one active flow at a time. A. Achievable Throughput of a Flow We first verify the accuracy of throughput estimation at saturation level. Three ACs are used with CWmin sizes of 15, 31 and 63. Each AC consists of 4 CBR flows from 4 stations with unlimited data source and there are a total of 12 active flows. CWmax sizes for all three ACs are set to 123. AIFS is set equal to DIFS. Five simulation runs are conducted with varied MAC service data unit (MSDU) sizes from 256 to 248B. The achievable throughput for each AC is estimated and compared with simulation results. Throughput of only one stream for each AC is shown in Fig. 4 to ease comparison. saturation throughput (bytes/sec) 45 4 35 3 25 2 15 1 5 Simulation Simulation Simulation Estimate Estimate Estimate 2 4 6 8 1 12 14 16 18 2 MSDU size (bytes) Fig. 4: Estimated achievable throughput closely matches simulation results for different ACs. As seen from these results, the algorithm provides a good estimation of the achievable throughput of an AC. It is clear that high priority ACs, with smaller contention windows and larger MSDU sizes (or TXOP durations), will have a higher throughput when the channel is fully utilized. B. Effectiveness of Admission Control Algorithm The simulation starts with 15 stations and 15 CBR flows, 5 at 1.8Mbps with 1B packets and CWmin=13, 5 at 5kbps with 5B packets and CWmin=31, and 5 at 8kbps with 1B packets and CWmin=4. The CWmin values are determined by the admission control algorithm. RTS/CTS mechanism is used. These 15 flows are admitted to the service set and they reach the achievable throughput limit indicated by the admission control algorithm. The reason CBR traffic is chosen in these simulations is because we can accurately evaluate the achievable throughput limits estimated by the algorithm, by setting each flow transmitting close to the predicted throughput limit. In case of variable bit rate traffic, similar performance can be achieved by limiting the offered traffic of each flow below its estimated limit. Depending on the network situation, further traffic engineering tools can be applied (e.g. leaky bucket) to reduce the burstiness and allow traffic to transmit above the limits momentarily, thereby increasing efficiency. We now focus on the region when the channel is fully utilized. We would like to study the effect of admitting a new flow when the admission control indicates we should not do so. 2 seconds into the simulation, an additional flow (flow16) at 1.8 Mbps and packet size of 1B (CWmin=15) starts transmission. The result is that all flows experienced quality degradation due to channel overload. We identified two regions for investigation, the flow phase from 5 to 2 seconds when all flows are transmitting below their estimated achievable throughput limits, and the saturation phase from 2 seconds onwards when the new flow causes the limit to be exceeded. throughput (bytes/sec) 35 3 25 2 15 1 5 flow 1 18Kbps flow 2 18Kbps flow 6 18Kbps flow 7 5Kbps flow 8 5Kbps flow 12 8Kbps flow 13 8Kbps Flow Phase Saturation Phase 5 1 15 2 25 3 35 4 time (sec) Fig. 5: Throughput performance with admission control enabled (t = to 2s) and disabled (t = 2 to 4s) cumulative distribution (%) 1 9 8 7 6 5 4 3 2 1 High Priority Medium Priority Low Priority flow 1 flow 2 flow 3 flow 4 flow 5 flow 6 flow 7 flow 8 flow 9 flow 1 flow 11 flow 12 flow 13 flow 14 flow 15.2.4.6.8.1.12 latency (sec) Fig. 6: Medium access delays during flow phase
cumulative distribution (%) 1 9 8 7 6 5 4 3 2 1 High priority Medium and low priorities flow 1 flow 2 flow 3 flow 4 flow 5 flow 6 flow 7 flow 8 flow 9 flow 1 flow 11 flow 12 flow 13 flow 14 flow 15 flow 16.2.4.6.8 1 1.2 1.4 latency (sec) Fig. 7: Medium access delays during saturation phase Throughput performance is shown in Fig. 5 during the 35 second period. Throughput is stable for the first 15 seconds indicating the medium is capable of handling the traffic. However, we can see that the bandwidth fluctuates greatly in the saturation phase due to random backoff. Comparing this graph with Fig. 2, during the saturation phase (from 13 seconds onwards in Fig. 2), the flows that remain below their achievable throughput are unaffected. The situation is different in Fig. 5 since all flows are transmitting just below their achievable throughput. The effect of overloading extends to all flows, as the throughput of all flows starts to fluctuate. The implication of this effect can be applied to protect important flows by assigning higher achievable throughput to these flows than the offered traffic, thus providing a buffer to maintain QoS to some extents. The drawback is lower channel utilization and higher collision rates because of smaller contention window size and higher transmission probability. Fig. 6 shows the medium access delay distribution during the flow phase. Flows 11 to 15 have the worst delay performance but the maximum delay remains below 1ms. Fig. 7 shows the delay distribution during the saturation phase. The delay performance deteriorates rapidly as additional traffic is introduced to the network. Delay is greater than.6s for at least 1% of the traffic in the medium and low priority ACs. VI. RELATED WORK The Virtual MAC algorithm in [5] passively monitors the radio channel and estimates locally achievable service levels. The main criteria for the decision of admission control is based on delay and collision estimates. It does not provide any achievable throughput information, which is also useful to multimedia applications. Another drawback is that the virtual MAC algorithm computations complicate the mobile station. The Assured Rate MAC Extension proposed in [6] extends the assured rate service in Differentiated Services to 82.11 WLANs. This scheme achieves the desired throughput by dynamically adjusting the contention window size based on observation of the estimated sending and desired rates. In the case when the sending rate is below the desired rate, the contention window size is reduced to obtain a higher throughput. Along with other similar proposals [7], [8] for 82.11 MAC to support QoS, these schemes only provide relative priority and bandwidth of current flows are not protected. VII. CONCLUSION AND FUTURE WORK This paper highlights the difficulties of maintaining QoS in contention based 82.11 WLANs. By considering the interaction of frame transmissions in the MAC layer, we are able to predict the achievable throughput for each flow. This information provides a useful guiding point to efficiently manage bandwidth provision in EDCA by eliminating much of the guesswork. Simulation results showed our proposed admission control can effectively maintain the QoS of admitted flows by preventing any new flows overloading the channel. Throughput and delay performance remained at acceptable levels for real-time traffic as long as offered traffic is below the estimated limits. The CWmin parameter affects both the delay and bandwidth that a flow experiences. In order for a flow to receive low delay, it needs to have a low CWmin, which gives it a high throughput. However, applications like voice require lower delay and lower throughput than applications like video. One solution is to assign larger TXOP duration to a voice flow to allow it to transmit multiple packets in each successful contention. However, the delay jitter will increase as the first packet in a packet burst will experience a higher delay compared to the subsequent packets. The extent of delay jitter requires further study. However, simulation results demonstrate that even if TXOPs are not used, delay can remain at satisfactory level. The current draft of 82.11e [1] specifies a direct link protocol to allow direct communications between non-ap stations in infrastructure mode, upon registration with the AP. Our proposed admission control algorithm can be modified for used under this scheme, with the work of collision monitoring being shifted to the non-ap stations and passing the collision information to the AP using a signaling protocol. Please refer to [9] for details. Future studies will also include various channel error models to study the effectiveness of the admission control algorithm. ACKNOWLEDGEMENTS This work is supported by the Australian Research Council and industry partner SingTel Optus Pty Ltd. REFERENCES [1] IEEE Draft Standard 82.11e/ D4.4, June. 23. [2] G. Bianchi, Performance Analysis of the IEEE 82.11 Distributed Coordination Function, IEEE JSAC, 18(3): 535-47, Mar. 2. [3] S. Mangold et al., IEEE 82.11e Wireless LAN for Quality of Service, Proc. of European Wireless 22, pp.32-9. [4] The Network Simulator (ns-2), www.isi.edu/nsnam/ns/ [5] A. Veres et al., Supporting Service Differentiation in Wireless Packet Networks Using Distributed Control, IEEE JSAC 19(1):281-93, Oct.21. [6] A. Banchs, X. Perez, Providing Throughput Guarantees in IEEE 82.11 Wireless LAN, Proc. IEEE WCNC 22, Vol. 1, pp.13-8. [7] K. Saitoh et al., An Effective Data Transfer Method by Integrating Priority Control into Multirate Mechanisms for IEEE 82.11 Wireless LANs, Proc. IEEE VTC 22, Vol 1, pp.55-9. [8] D. Qiao and K. G. Shin, Achieving Efficient Channel Utilization and Weighted Fairness for Data Communications in IEEE 82.11 WLAN under the DCF, IEEE Int l Workshop on QoS, 22, pp.227-36. [9] D. Pong, Technical Report - Admission control for IEEE82.11e EDCF, March 23. (http://uluru.ee.unsw.edu.au/ dennis/adcontrol8211.pdf)