Adaptive fair channel allocation for QoS enhancement in IEEE 802.11 wireless LANs

The emerging widespread use of real-time multimedia applications over wireless networks makes the support of quality of service (QoS) a key problem. In this paper, we focus on QoS support mechanisms for IEEE 802.11 wireless ad-hoc networks. First, we review limitations of the upcoming IEEE 802.11e enhanced DCF (EDCF) and other enhanced MAC schemes that have been proposed to support QoS for 802.11 ad-hoc networks. Then, we describe a new scheme called adaptive fair EDCF that extends EDCF, by increasing the contention window during deferring periods when the channel is busy, and by using an adaptive fast backoff mechanism when the channel is idle. Our scheme computes an adaptive backoff threshold for each priority level by taking into account the channel load. The new scheme significantly improves the quality of multimedia applications. Moreover, it increases the overall throughput obtained both in medium and high load cases. Simulution results show that our new scheme outperforms EDCF and other enhanced schemes. Finally, we show that the adaptive fair EDCF scheme achieves a high degree of fairness among applications of the same priority level

Decentralised Learning MACs for Collision-free Access in WLANs

By combining the features of CSMA and TDMA, fully decentralised WLAN MAC schemes have recently been proposed that converge to collision-free schedules. In this paper we describe a MAC with optimal long-run throughput that is almost decentralised. We then design two \changed{schemes} that are practically realisable, decentralised approximations of this optimal scheme and operate with different amounts of sensing information. We achieve this by (1) introducing learning algorithms that can substantially speed up convergence to collision free operation; (2) developing a decentralised schedule length adaptation scheme that provides long-run fair (uniform) access to the medium while maintaining collision-free access for arbitrary numbers of stations

Enhanced Collision Resolution for the IEEE 802.11 Distributed Coordination Function

The IEEE 802.11 standard relies on the Distributed Coordination Function (DCF) as the fundamental medium access control method. DCF uses the Binary Exponential Backoff (BEB) algorithm to regulate channel access. The backoff period determined by BEB depends on a contention window (CW) whose size is doubled if a station suffers a collision and reset to its minimum value after a successful transmission. BEB doubles the CW size upon collision to reduce the collision probability in retransmission. However, this CW increase reduces channel access time because stations will spend more time sensing the channel rather than accessing it. Although resetting the CW to its minimum value increases channel access, it negatively affects fairness because it favours successfully transmitting stations over stations suffering from collisions. Moreover, resetting CW leads to increasing the collision probability and therefore increases the number of collisions. % Quality control editor: Please ensure that the intended meaning has been maintained in the edits of the previous sentence. Since increasing channel access time and reducing the probability of collisions are important factors to improve the DCF performance, and they conflict with each other, improving one will have an adverse effect on the other and consequently will harm the DCF performance. We propose an algorithm, \gls{ECRA}, that solves collisions once they occur without instantly increasing the CW size. Our algorithm reduces the collision probability without affecting channel access time. We also propose an accurate analytical model that allows comparing the theoretical saturation and maximum throughputs of our algorithm with those of benchmark algorithms. Our model uses a collision probability that is dependent on the station transmission history and thus provides a precise estimation of the probability that a station transmits in a random timeslot, which results in a more accurate throughput analysis. We present extensive simulations for fixed and mobile scenarios. The results show that on average, our algorithm outperformed BEB in terms of throughput and fairness. Compared to other benchmark algorithms, our algorithm improved, on average, throughput and delay performance