On the Throughput Maximization in Dencentralized Wireless Networks

A distributed single-hop wireless network with K links is considered, where the links are partitioned into a fixed number (M ) of clusters each operating in a subchannel with bandwidth W M . The subchannels are assumed to be orthogonal to each other. A general shadow-fading model, described by parameters (α,̟), is considered where α denotes the probability of shadowing and ̟ (̟ ≤ 1) represents the average cross-link gains. The main goal of this paper is to find the maximum network throughput in the asymptotic regime of K → ∞, which is achieved by: i) proposing a distributed and non-iterative power allocation strategy, where the objective of each user is to maximize its best estimate (based on its local information, i.e., direct channel gain) of the average network throughput, and ii) choosing the optimum value for M . In the first part of the paper, the network throughput is defined as the average sum-rate of the network, which is shown to scale as Θ(logK). Moreover, it is proved that in the strong interference scenario, the optimum power allocation strategy for each user is a threshold-based on-off scheme. In the second part, the network throughput is defined as the guaranteed sum-rate, when the outage probability approaches zero. In this scenario, it is demonstrated that the on-off power allocation scheme maximizes the throughput, which scales as W α̟ logK . Moreover, the optimum spectrum sharing for maximizing the average sum-rate and the guaranteed sum-rate is achieved at M = 1. Index Terms Throughput maximization, distributed power allocation, shadow-fading, wireless network. ∗ This work is financially supported by Nortel Networks and the corresponding matching funds by the Natural Sciences and Engineering Research Council of Canada (NSERC), and Ontario Centers of Excellence (OCE). ∗ The material in this paper was presented in part at the IEEE International Symposium on Information Theory (ISIT), Nice, France, June 24-29, 2007 [1] and the 41th Conference on IEEE Information Sciences and Systems (CISS), Johns Hopkins University, Baltimore, MD, March 14-16, 2007 [2]. 2