Evolutionary Game and Learning for Dynamic Spectrum Access

TL;DR

This paper introduces evolutionary game theory and reinforcement learning approaches for dynamic spectrum access, improving spectrum utilization and fairness in wireless networks under different information scenarios.

Contribution

It proposes a novel evolutionary spectrum access mechanism and a distributed reinforcement learning method for dynamic spectrum access, addressing both complete and incomplete network information.

Findings

Achieves up to 82% performance improvement over random access.

Converges to an approximate Nash equilibrium.

Robust to random perturbations in channel selection.

Abstract

Efficient dynamic spectrum access mechanism is crucial for improving the spectrum utilization. In this paper, we consider the dynamic spectrum access mechanism design with both complete and incomplete network information. When the network information is available, we propose an evolutionary spectrum access mechanism. We use the replicator dynamics to study the dynamics of channel selections, and show that the mechanism achieves an equilibrium that is an evolutionarily stable strategy and is also max-min fair. With incomplete network information, we propose a distributed reinforcement learning mechanism for dynamic spectrum access. Each secondary user applies the maximum likelihood estimation method to estimate its expected payoff based on the local observations, and learns to adjust its mixed strategy for channel selections adaptively over time. We study the convergence of the learning mechanism based on the theory of stochastic approximation, and show that it globally converges to an approximate Nash equilibrium. Numerical results show that the proposed evolutionary spectrum access and distributed reinforcement learning mechanisms achieve up to 82% and 70% performance improvement than a random access mechanism, respectively, and are robust to random perturbations of channel selections.