Let Cognitive Radios Imitate: Imitation-based Spectrum Access for Cognitive Radio Networks

TL;DR

This paper introduces imitation-based distributed spectrum access policies for cognitive radio networks, leveraging evolutionary game theory to enable secondary users to efficiently access channels with unknown availability, ensuring convergence to stable and near-optimal equilibria.

Contribution

The paper proposes novel imitation-based spectrum access policies using Proportional and Double Imitation rules, with theoretical analysis of their convergence and stability in cognitive radio networks.

Findings

Policies converge to an imitation-stable equilibrium

Equilibrium approximates the system's optimal performance

Policies are implementable with local information

Abstract

In this paper, we tackle the problem of opportunistic spectrum access in large-scale cognitive radio networks, where the unlicensed Secondary Users (SU) access the frequency channels partially occupied by the licensed Primary Users (PU). Each channel is characterized by an availability probability unknown to the SUs. We apply evolutionary game theory to model the spectrum access problem and develop distributed spectrum access policies based on imitation, a behavior rule widely applied in human societies consisting of imitating successful behavior. We first develop two imitation-based spectrum access policies based on the basic Proportional Imitation (PI) rule and the more advanced Double Imitation (DI) rule given that a SU can imitate any other SUs. We then adapt the proposed policies to a more practical scenario where a SU can only imitate the other SUs operating on the same channel. A systematic theoretical analysis is presented for both scenarios on the induced imitation dynamics and the convergence properties of the proposed policies to an imitation-stable equilibrium, which is also the $\epsilon$-optimum of the system. Simple, natural and incentive-compatible, the proposed imitation-based spectrum access policies can be implemented distributedly based on solely local interactions and thus is especially suited in decentralized adaptive learning environments as cognitive radio networks.