Abstract
Link scheduling plays a key role in the network capacity and the transmission delay. In this paper, we study the problem of maximum link scheduling (MLS), aiming to characterize the maximum number of links that can be successfully scheduled simultaneously under Rayleigh-fading and multiuser interference. After analyzing the minimum distance between successful links in the existing GHW scheduling algorithm, we propose a DLS (Distance-based Link Scheduling) algorithm. Then, the global interference is characterized and bounded by introducing a separation distance between selected links, building on which we propose a distributed version of DLS (denoted by DDLS) that converges to a constant factor of the non-fading optimum within time complexity $O(n\ln n)$ , where $n$ is the number of links. Furthermore, we study the Shortest Link Scheduling (SLS) problem, which minimizes the number of time slots to successfully schedule each link for at least once. An algorithm for SLS with approximation factor of $O(\ln n)$ is obtained by executing DDLS. Extensive simulations show that DDLS greatly outperforms GHW and the other two popular algorithms.
Highlights
E FFECTIVE communication in wireless networks depends on successful reception in the presence of interference and noise, which is cast as the link scheduling problem
Under Rayleigh fading model, a link usually is scheduled with some probability, since the fading gain makes the strength of received signal uncertain
Based on the distance constraint given in Equation (4), we propose the centralized algorithm Distance-Based Link Scheduling (DLS) for maximum link scheduling (MLS) problem
Summary
E FFECTIVE communication in wireless networks depends on successful reception in the presence of interference and noise, which is cast as the link scheduling problem. Notice that in [2], it has been shown that any one of links in a SINR-feasible set (a set of links that can be scheduled simultaneously in the SINR model; please refer to Section III for the detailed definition) is scheduled in the Rayleigh-fading model with probability 1/e. This is far from enough when a higher success probability is required. The MLS in the Rayleigh-fading model can be reduced to the following one: given a guarantee requirement of success probability, remove all non-intended senders for the receiver of each selected link and obtain a SINR-feasible set.
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