Evolutionarily Stable Spectrum Access

TL;DR

This paper introduces distributed spectrum access mechanisms that adaptively reach stable equilibria using evolutionary strategies and learning, effective under both complete and incomplete network information.

Contribution

It presents a novel evolutionary spectrum access mechanism with proven global stability and a distributed learning approach that converges to the same equilibrium with limited information.

Findings

Mechanisms achieve globally evolutionarily stable equilibria.

Learning converges to the same equilibrium on average.

Proposed methods are robust to user perturbations.

Abstract

In this paper, we design distributed spectrum access mechanisms with both complete and incomplete network information. We propose an evolutionary spectrum access mechanism with complete network information, and show that the mechanism achieves an equilibrium that is globally evolutionarily stable. With incomplete network information, we propose a distributed learning mechanism, where each user utilizes local observations to estimate the expected throughput and learns to adjust its spectrum access strategy adaptively over time. We show that the learning mechanism converges to the same evolutionary equilibrium on the time average. Numerical results show that the proposed mechanisms are robust to the perturbations of users' channel selections.