Distributed Throughput Maximization in Wireless Networks via Random Power Allocation

Abstract

Abstract-We develop a distributed throughput-optimal power allocation algorithm in wireless networks. The study of this problem has been limited due to the non-convexity of the underlying optimization problems, that prohibits an efficient solution even in a centralized setting. By generalizing the randomization framework originally proposed for input queued switches to SINR rate-based interference model, we characterize the throughput-optimality conditions that enable efficient and distributed implementation. Using gossiping algorithm, we develop a distributed power allocation algorithm that satisfies the optimality conditions, thereby achieving (nearly) 100% throughput. We illustrate the performance of our power allocation solution through numerical simulation.Index Terms-Throughput-optimal power allocation, randomization framework, SINR-based interference model.

A fully distributed algorithm for throughput performance in wireless networks

“…Often distributed algorithms for wireless networks have to assume another underlying information infrastructure that can be used to run a localized and/or distributed algorithm, and that infrastructure, moreover, is not subject to the interference constraints of the original network. This is the case with [14], as well as many other works on the topic ( [18,15] for example). This is a rather strong assumption, especially in light of the fact that in a wireless network, one is usually trying to establish such an infrastructure in the first place.…”

“…The authors show that the basic LQF is not efficient, but a variation of it that localizes interference is shown to work, with efficiency similar to ours. In a related work, [15] also consider the SINR model, but the model there is different (links are always feasible, but have different data rates based on the SINR achieved. This sort of problem is rather different from the "combinatorial" situation at hand).…”

Token-Based Function Computation with Memory

“…As another application, consider a network with n processors in which each processor has a local utility function and the goal is to obtain the optimal solution of sum of the utility functions subject to some constraints. This problem has frequently arisen in network optimization algorithms such as distributed learning [4], link scheduling [5], and network utility maximization [6]. All these algorithms utilize a distributed sum or average computation subroutine in solving the optimization problems.…”