MAC approaches for QoS Enhancement in Wireless LANs - CiteSeerX

MAC approaches for QoS Enhancement in Wireless LANs José André Moura and Rui Neto Marinheiro ADETTI/ISCTE Av. das Forças Armadas, Edifício ISCTE, 1600-082 Lisboa Telefone: +351.217.903.099, Fax: +351.217.903.099

Abstract. Demanding for real-time multimedia applications in wireless access is increasing, and this has driven recent research in QoS. The upcoming IEEE 802.11e standard will give a firm impulse towards QoS provisioning in 802.11 wireless LANs. In parallel, during the last years, other enhanced MAC schemes have also been proposed to improve QoS metrics. Some of these approaches modify the 802.11e Backoff procedure, either by changing the way the Contention Window is determined or by changing the way the backoff timer is decreased. Supported by these previous works, we describe three new approaches. One of them is designated by Differentiated Exponential Collision Recover – DECR, which uses exponential decrease functions in the backoff timer, with different exponential decrease rate for each Access Category. Using simulation results, we conclude that DECR, when compared with other approaches, improves channel utilization without significantly increasing the collision rate.

Keywords: Medium Access Control (MAC), Backoff, Service differentiation, Wireless LAN, IEEE 802.11e, Quality of Service (QoS)

1. Introduction Nowadays the IEEE 802.11 [i] represents the most exploited protocol for the deployment of Wireless LANs. However, its DCF mode only provides channel access with equal probability to all contending stations or traffic types, not supporting the QoS provisioning. The upcoming EDCA [ii] mode of IEEE 802.11e has been designed to provide eight different user priorities (from 0 to 7) level. As one can see in Table 1, those user priorities are mapped to four Access Categories (ACs). In this way, each IEEE 802.11e station has four packet queues, one for each access category, with four channel access functions. In Fig. 1, we present the time diagram for a typical EDCA channel access function. It uses new parameters, namely AIFS[AC], CWmin[AC], and CWmax[AC] instead of previous DCF corresponding ones as DIFS, CWmin, and CWmax, respectively, for ensuring different EDCA service for each priority level. The AIFS[AC] parameter is used for giving opportunity to the flows with higher priority to contend the medium without the interference of other flows with lower priority level. The parameters CWmin[AC] and CWmax[AC] are used for calculating a CW range window [0, CW] that is used for generate a random backoff, in order to ensure a collision avoidance mechanism. When a new packet belonging to a AC traffic type is delivered to the corresponding queue and the medium maintains idle during at least AIFS[AC] time, the EDCA function transmits the packet, if: a) the backoff timer for that EDCA function has a value of zero, and b) the actual transmission sequence is not contending the channel with an EDCA function of a higher priority.

Table 1 – User priority to Access Category mappings

Fig. 1 - IEEE 802.11e EDCA channel access

When the station senses the medium busy, the EDCA function defers the packet transmission till the moment the medium becomes idle again and the backoff timer has a value of zero. Our goal is to study the performance of several MAC algorithms, some already proposed and other suggested in this paper. They were implemented in NS-2[iii]. We have used a test scenario of a Wireless LAN, without mobility and multihop routing, with incremented number of nodes, sending three flows of different priority level to one receiving node. The rest of the paper is organized as follows: in section 2, will briefly describe related work; in section 3, several MAC QoS implementations are described; in section 4, simulation results of those schemes are presented and discussed; in section 5, some conclusions and future work are presented.

2. Related Work Surveys can be found in [iv][v], where classification is done about many approaches that enhance MAC protocols in order to provide QoS. [v] compares several service differentiation based schemas. Those schemas are classified as station and queue based, and also as PCF and DCF based. In this present research, we are only interested in comparing some Queue-DCF based approaches. [iv] shows a hierarchical taxonomy of QoS mechanisms that enable service differentiation in 802.11 networks. Following their taxonomy, the approaches compared in this paper can be classified as priority-based methodologies, using backoff algorithms or contention window differentiation. In particular, we have seen recently some approaches that modify the Backoff Procedure, either by changing the way the Contention Window is determined or by changing the way the backoff timer is decreased, introducing an exponential behavior on their algorithms. We can see examples of the former case in the AEDCF[vi], GDCF[vii], EDCF-DM[viii] approaches. Examples of the later case have been address in the FCR[ix], AFEDCF[x] approaches. AEDCF[vi] adjusts the contention window size of each traffic class taking into account the channel collision rate. Their approach has improved total goodput, but recent results suggest that low-priority flows degrade at high load [x]. GDCF[vii] changed the way the contention window is decreased, after successful transmissions. Instead of resetting CW to CWmin, they introduced an exponential decreasing algorithm using steps. However, their approach does not support traffic type differentiation. EDCF-DM[viii] try to improve AEDCF[vi], by taking into account, not only the network condition of the system, but also the traffic state of each traffic category at each active node. EDCF-DM approach outperforms AEDCF, when the throughput is considered. However, AEDCF presents better mean delays. The FCR[ix] schema uses a fast backoff timer decrease mechanism, with a static backoff threshold. Above the threshold, the backoff timer is decreased linearly, and bellows the threshold it decreases exponentially. AFEDCF[x] was inspired by the previous approach, by considering access categories for different traffic types, and by adjusting dynamically the threshold value, according to the network status.

3. MAC approaches for QoS enhancement In this work, we have looked into several approaches that implement QoS in 802.11 wireless networks. Three of them are new proposed approaches. Two of them use a backoff timer exponential decrease functions and the third one uses a contention window exponential decrease function. The first approach uses

an Exponential Collision Recover (ECR) mechanism. Our approach does not use the two-backoff decrease stage as proposed in [x] but only a unique decrease function that decreases exponentially the backoff timer for each idle slot. The differences between our proposal and the 802.11e draft [ii] are the following: •

Our proposal, during idle slots, decreases the backoff timer exponentially and the draft decreases it linearly. With this proposal, we aim to reduce the idle time, improving channel utilization. The initial backoff is selected randomly from [0, CW[AC]], but since the backoff decrease is exponential, in practice the distribution of waiting times for channel access it is not uniform but logarithmic, distributed between 0 and log2(CW[AC]). Therefore, stations with a higher initial backoff will experience significantly shorter waiting periods until next channel access. In addition, collisions will not increase significantly for stations with a lower initial backoff, because these stations will have a scarcer distribution for the same effective waiting time.

•

Our proposal increases the contention window during collision and during defer periods. The draft only increases it during collision periods. Our goal here is to penalize the low priority queue and improve the fairness index between the same priority queues [x].

The second approach uses a Differentiated Exponential Collision Recover (DECR) mechanism, similar to the exponential backoff decrease used in ECR. The difference here is that it decreases faster for lower priority level traffic than for higher traffic levels. Both ECR and DECR, have the same behavior as suggested in 802.11e draft [ii]: after a successful packet transmission, belonging to a certain priority level, it decreases the associated contention window to its minimum value. The third approach is an enhanced version of GDCF and follows the original GDCF [vii] algorithm, since its original implementation does not support priority service differentiation. Our implementation does it, using exponential decrease functions for the contention window with different width steps, one for each type of traffic: high priority, medium priority and low priority. The differences between our version of GDCF and the 802.11e draft [ii] are the following: • Our proposal decreases the collision probability [vii], because it decreases more slowly the contention window than the legacy implementation in the following situations: o

after receiving an ACK message;

o

after reaching the maximum number of retransmissions of one packet and the inevitable drop action over it.

• Our proposal also tries to improve fairness metric because it maintains all the nodes in the same stage (with the same CW) even if after several consecutive successful transmissions [vii].

4. Simulation tests We have implemented all the schemes in NS-21[iii], and tested with different wireless network topologies, in order to evaluate performance for the different QoS flows. Each node sends three different QoS flows (audio, video, and background traffic) to a unique receiver, as shown in the scenario of Fig. 2.

1

Our ns simulation code has been adapted from the AFEDCF code available on http://www-sop.inria.fr/planete/software/.

Node 1 A - Audio V

A

V - Video

B

B - Background A

B V

V Node 0

Node 2 A

Node n

B

Fig. 2 – Simulated Network Topology The physical data rate was set to 36 Mb/s. For parameter settings we have followed the default EDCA suggestion, as indicated in Table 2. We have considered for aCWmax the value of 1023 and for aCWmin 31, which gives, for all experiments, the instance values presented in Table 3. Each simulation had run for 15 seconds and the results shown are averages over three simulations with different flow starting times. AC AC_ BK AC_ BE AC_ VI AC_ VO

CWmin aCWmin

CWmax aCWmax

AIFS 7

aCWmin

aCWmax

3

(aCWmin+1)/2-1

aCWmin

2

(aCWmin+1)/4-1

(aCWmin+1)/2-1

2

Transport Priority CWmin CWmax AIFSN Packet Size Packet Interval Flow rate

Table 2 – EDCA default parameter settings

Audio (AC_VO) UDP 3 7 15 2 160 bytes

Video (AC_VI) UDP 2 15 31 2 1280 bytes

Background (AC_BK) UDP 0 31 1023 7 1500 bytes

20 ms

10 ms

12,5 ms

8 Kbyte/s

128 Kbyte/s

120 Kbyte/s

Table 3 - Parameters for each load

Our results comparing EDCA, AFEDCF, ECR, DECR and enhanced GDCF when considering total and background goodput are presented respectively

in Fig. 3 and 4, where one can observe that DECR

outperforms the other protocols, specially at high loads. 2600

120

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2200 2000

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DECR

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800 4

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Fig. 3 - Total goodput (KBytes/s)

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Fig. 4 - Background goodput (KBytes/s)

Our results comparing mean audio delay, mean video delay and mean background delay for EDCA, AFEDCF, ECR, DECR and enhanced GDCF are presented in figures 5, 6 and 7. Clearly, the EDCA for audio and video is the algorithm that has worst behavior, but for background traffic all algorithms have a similar behavior.

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8 AFEDCF ECR DECR EDCA GDCF

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55 50 45 EDCA AFEDCF ECR DECR GDCF

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Fig. 8 – Channel utilization (%)

Fig. 5 – Mean audio delay (ms) 6

1200

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EDCA AFEDCF ECR DECR GDCF

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800 600 400

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0,998

AFEDCF ECR DECR EDCA GDCF

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Fig. 9 – Drops per second

Fig. 6 – Mean video delay (ms) 60

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EDCA AFEDCF ECR DECR GDCF

0,994

30 20

0,992

10 0,990

0 4

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Fig. 10 – Video Fairness Index

Fig. 7 – Mean background delay (ms)

Our results about total channel utilization are in Fig. 8. Clearly, the algorithm that outperforms the others is the DECR, with values over 60% of channel utilization for 14 and 16 nodes. Our results about packet drop rate are shown in Fig. 9. Here the worst algorithm is the EDCA and the others have a similar behavior, with DECR behaving slightly better. We have also used Jain’s fairness index [xi] to evaluate the degree of fairness for each algorithm. 1,00 0,99 0,98 0,97 EDCA AFEDCF ECR DECR GDCF

0,96 0,95 0,94 0,93 0,92 4

6

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Fig. 11 – Background Fairness Index Our results comparing the fairness of video and background traffic for EDCA, AFEDCF, ECR, DECR and

enhanced GDCF are given in Fig. 10 and 11. For video traffic all algorithms over 16 nodes slightly decrease the fairness index, and also EDCA has the worst performance. For background traffic, the AFEDCF approach has a marginal better performance. For audio, we conclude that all approaches distribute traffic fairly to all contending nodes, and for this reason, we have not included its graphic. Therefore, we can conclude that DECR performs better than EDCA when the network is highly loaded. In this way, using DECR we increase the channel utilization without increasing the collisions and without compromising the traffic delay and the Fairness Index.

5. Conclusions and Future Work We have shown that using a differentiated approach for the exponential backoff decrease, for each access category, as suggested in the DECR schema, significantly improves goodput and channel utilization. DECR also reduces the number of drops per second, when compared with other approaches. DECR keeps the Fairness Index and mean delays at good levels, especially for the audio and video traffic types. With these results, we can hint that exponentially backoff timer decrease algorithm works better than mixed decrease modes, as suggested in AFEDCF, or than exponential decrease of contention window as determined in GDCF, even considering traffic differentiation. At present, we are studying the impact of certain MAC parameters, as suggested in [xii], to optimize the overall system performance, with DECR and other schemas.

References [i] IEEE 802.11 WG, IEEE Std 802.11, 1999 edition. International standard for Information Technology. Telecommunications and information exchange between systems - Local and metropolitan area networks. Specific Requirements Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) specifications, 1999 [ii] IEEE WG, Draft Supplement to Standard for Telecommunications and Information Exchange between SystemsLAN/MAN Specific Requirements- Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications: Medium Access Control (MAC), Enhancements for Quality of Service (QoS), 802.11e Draft 9.0, August, 2004 [iii] NS-2 simulator. http://www.isi.edu/nsnam/ns/ [iv] H. Zhu, M. Li, I. Chlamtac and B. Prabhakaran, A Survey of Quality of Service in IEEE 802.11 Networks, IEEE Wireless Communications, August 2004 [v] Q. Ni, L. Romdhani, and T. Turletti,, A Survey of QoS Enhancements for IEEE 802.11 Wireless LAN, Journal of Wireless Communications and Mobile Computing, John Wiley, vol.4, pp. 1-20, 2004 [vi] L. Romdhani, Q. Ni, and T. Turletti, Adaptive EDCF: Enhanced Service Differentiation for IEEE 802.11 Wireless Ad Hoc Networks, IEEE WCNC’03 (Wireless Communications and Networking Conference), New Orleans, Louisiana, March 16-20, 2003 [vii] Chonggang Wang, Bo Li, Lemin Li, A New Collision Resolution Mechanism to Enhance the Performance of IEEE 802_11 DCF, IEEE Transactions on Vehicular Technology, VOL. 53, NO. 4, JULY 2004 [viii] H. Zhu, G. Cao, A. Yener, A. Mathias, EDCF-DM: A Novel Enhanced Distributed Coordination Function for Wireless Ad Hoc Networks, IEEE communications Society, 2004 [ix] Y. Kwon, Y. Fang, and H. Latchman, A Novel MAC Protocol with Fast Collision Resolution for Wireless LANs, IEEE Infocom 2003, 2003 [x] M. Malli, Q. Ni, T. Turletti, C. Barakat, Adaptive Fair Channel Allocation for QoS Enhancement in IEEE 802.11 Wireless LANs, IEEE International Conference on Communications (ICC), June, 2004 [xi] Raj Jain, Arjan Durresi, Gojko Babic, "Throughput Fairness Index: An Explanation," ATM_Forum/99-0045, February 1999 [xii] Luca Scalia, Ilenia Tinnirello, “Differentiation mechanisms for heterogeneous traffic integration in IEEE 802.11 networks”, Broadband Wireless Multimedia (BroadWIM), 2004