Distributed Spectrum Access with Spatial Reuse

TL;DR

This paper develops distributed algorithms for spectrum sharing that incorporate spatial reuse, modeling the problem as potential games and proposing learning and mobility strategies that converge to Nash equilibria.

Contribution

It extends the spatial congestion game framework to include joint channel and location selection, introducing distributed algorithms with proven convergence.

Findings

The proposed algorithms converge to Nash equilibria using local observations.

Modeling the problem as potential games ensures convergence and stability.

Joint channel and mobility strategies improve spectrum utilization.

Abstract

Efficient distributed spectrum sharing mechanism is crucial for improving the spectrum utilization. The spatial aspect of spectrum sharing, however, is less understood than many other aspects. In this paper, we generalize a recently proposed spatial congestion game framework to design efficient distributed spectrum access mechanisms with spatial reuse. We first propose a spatial channel selection game to model the distributed channel selection problem with fixed user locations. We show that the game is a potential game, and develop a distributed learning mechanism that converges to a Nash equilibrium only based on users' local observations. We then formulate the joint channel and location selection problem as a spatial channel selection and mobility game, and show that it is also a potential game. We next propose a distributed strategic mobility algorithm, jointly with the distributed learning mechanism, that can converge to a Nash equilibrium.