Distributed Learning Algorithms for Spectrum Sharing in Spatial Random Access Wireless Networks

TL;DR

This paper develops and analyzes distributed algorithms for spectrum sharing in spatial random access wireless networks, addressing both non-cooperative and cooperative scenarios to optimize user rates.

Contribution

It introduces simple distributed learning algorithms tailored for spectrum sharing, with theoretical and simulation validation for both non-cooperative and cooperative cases.

Findings

Algorithms converge to equilibrium states

Achieve proportional fairness among users

Validated through theoretical analysis and simulations

Abstract

We consider distributed optimization over orthogonal collision channels in spatial random access networks. Users are spatially distributed and each user is in the interference range of a few other users. Each user is allowed to transmit over a subset of the shared channels with a certain attempt probability. We study both the non-cooperative and cooperative settings. In the former, the goal of each user is to maximize its own rate irrespective of the utilities of other users. In the latter, the goal is to achieve proportionally fair rates among users. Simple distributed learning algorithms are developed to solve these problems. The efficiencies of the proposed algorithms are demonstrated via both theoretical analysis and simulation results.