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 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.
Recently, @cite_3 proposed Learning-BEB, based on a modification of the conventional 802.11 DCF. In a decentralised fashion, it ultimately achieves collision-free TDMA-like operation for all stations The basic principle of its operation is that similarly to the 802.11 DCF, stations use a backoff counter and transmit after observing that number of idle slots. However, in Learning-BEB all stations choose a fixed, rather than random, value for the backoff counter after a successful transmission. After a colliding transmission, they choose the backoff counter uniformly at random, as in the DCF. We can think of this as each station randomly choosing a slot in a schedule, until they all choose a distinct slot. Arriving at this collision-free schedule can take a substantial period of time. In particular, when the number of slots in a schedule is close to the number of stations, it will take an extremely long time to converge to collision-free scenario. The authors of @cite_19 propose a scheme, SRB, that is similar in spirit to L-BEB.
@cite_19 @cite_3
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This paper proposes a semi-random backoff (SRB) method that enables resource reservation in contention-based wireless LANs. The proposed SRB is fundamentally different from traditional random backoff methods because it provides an easy migration path from random backoffs to deterministic slot assignments. The central idea of the SRB is for the wireless station to set its backoff counter to a deterministic value upon a successful packet transmission. This deterministic value will allow the station to reuse the time-slot in consecutive backoff cycles. When multiple stations with successful packet transmissions reuse their respective time-slots, the collision probability is reduced, and the channel achieves the equivalence of resource reservation. In case of a failed packet transmission, a station will revert to the standard random backoff method and probe for a new available time-slot. The proposed SRB method can be readily applied to both 802.11 DCF and 802.11e EDCA networks with minimum modification to the existing DCF EDCA implementations. Theoretical analysis and simulation results validate the superior performance of the SRB for small-scale and heavily loaded wireless LANs. When combined with an adaptive mechanism and a persistent backoff process, SRB can also be effective for large-scale and lightly loaded wireless networks. Abstract —Random access protocols have been the mechanismof choice for most WLANs, thanks to their simplicity anddistributed nature. Nevertheless, these advantages come at theprice of sub-optimal channel utilization because of empty slotsand collisions. In previous random access protocols, the stationstransmit on the channel without any clue of other stations’intentions to transmit. In this article we provide a framework tostudy the efﬁciency of channel access protocols. This frameworkis used to analyze the efﬁciency of the Binary Exponential Backoffmechanism and the maximum achievable efﬁciency that can beobtained from any completely random access protocol. Then wepropose Learning-BEB (L-BEB).L-BEB is exactly the same as legacy BEB, with one exception:L-BEB chooses a deterministic backoff value after a successfultransmission. We call this value the virtual frame size ( V ). Thissubtle modiﬁcation signiﬁcantly reduces the number of collisions.It can be observed that, as the system runs, the number of colli-sions is progressively reduced. Thus we conclude that the systemlearns. Further, if the number of contending stations is equal orlower than