Scheduling for next generation WLANs: filling the gap between offered and observed data rates

In wireless networks, opportunistic scheduling is used to increase system throughput by exploiting multi-user diversity. Although recent advances have increased physical layer data rates supported in wireless local area networks (WLANs), actual throughput realized are significantly lower due to overhead. Accordingly, the frame aggregation concept is used in next generation WLANs to improve efficiency. However, with frame aggregation, traditional opportunistic schemes are no longer optimal. In this paper, we propose schedulers that take queue and channel conditions into account jointly, to maximize throughput observed at the users for next generation WLANs. We also extend this work to design two schedulers that perform block scheduling for maximizing network throughput over multiple transmission sequences. For these schedulers, which make decisions over long time durations, we model the system using queueing theory and determine users' temporal access proportions according to this model. Through detailed simulations, we show that all our proposed algorithms offer significant throughput improvement, better fairness, and much lower delay compared with traditional opportunistic schedulers, facilitating the practical use of the evolving standard for next generation wireless networks

Achievable bandwidth estimation for stations in multi-rate IEEE 802.11 WLAN cells

This paper analyzes the effect of multi-rate transmissions in a CSMA wireless LAN environment. Observations in a real testbed showed that bandwidth resources (in Bytes/s) are shared fairly among all stations even though transmissions carried out at lower rates capture the medium for longer periods, which drastically reduces the overall throughput. The intrinsic concept of fairness in a CSMA scheme with multiple rates is quantified by means of a new formulation which is validated through simulations and practical measurements. The algorithm presented provides the maximum achievable bandwidth that can be offered to a given IEEE 802.11 station. Having this information has evident applications in realtime multimedia transmissions over WLANs. The algorithm was also run in commercial APs as a proof of concept, after analyzing its implementation issues

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