A joint congestion control, channel allocation and scheduling algorithm for multi-channel multi-interface multihop wireless networks is discussed. The goal of maximizing a utility function of the injected traffic, while guaranteeing queue stability, is defined as an optimization problem where the input traffic intensity, channel loads, interface to channel binding and transmission schedules are jointly optimized by a dynamic algorithm. Due to the inherent NP-Hardness of the scheduling problem, a simple centralized heuristic is used to define a lower bound for the performance of the whole optimization algorithm. The behavior of the algorithm for different numbers of channels, interfaces and traffic flows is shown through simulations.
Multicellular networks based on the IEEE 802.16 standard appear to be very promising candidates to provide end users with broadband wireless access. However, they also pose interesting challenges in terms of radio resource management, where several design choices are not specified in the standard, intentionally left open to implementors. For this reason, we focus in this article on scheduling and resource allocation, and investigate how they could operate in a crosslayer fashion. In particular, we describe the principles of joint scheduling and resource allocation for IEEE 802.16 operating in AMC mode, and discuss the critical role played by physical layer considerations, especially intercell interference estimation and channel state awareness, in the obtained performance. This leads to identifying key open issues and possible general solutions.
Rate Adaptation for 802.11 has been deeply investigated in the past, but the problem of achieving optimal Rate Adaptation with respect not only to channel-related errors but also to contention-related issues (i.e., collisions and variations in medium access times) is still unsolved. In this paper we address this issue by proposing 1) a practical definition of the Medium Status in a multi-user 802.11 scenario in terms of channel errors, MAC collisions and packet service times, and a method for its estimation based on measurements; 2) an analytical model of the goodput performance as a function of the Medium Status; 3) a rate adaptation algorithm, called Goodput Optimal Rate Adaptation (GORA), which is based on this model. Unlike other Rate Adaptation schemes proposed in literature, which require either modifications to the IEEE 802.11 standard or cooperation among nodes, GORA is totally stand-alone and standard compliant. In fact, the Medium Status Estimation used by GORA is obtained by using standard MAC counters that are commonly collected by commercial MAC drivers, and no explicit interactions with the other devices in the network is required. Therefore, GORA offers the advantage of being readily deployable on real devices. The performance of GORA is evaluated through NS2 simulations which reveal that, as expected, GORA outperforms other wellknown Rate Adaptation algorithms in several scenarios and can be used as a new reference benchmark.
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