Adaptive Channel Allocation Spectrum Etiquette for Cognitive Radio Networks

TL;DR

This paper introduces a game theoretic approach for adaptive spectrum sharing in cognitive radio networks, analyzing cooperative and selfish behaviors to improve network efficiency with different implementation strategies.

Contribution

It formulates spectrum sharing as a potential game and proposes a no-regret learning method for both cooperative and selfish scenarios, highlighting trade-offs in overhead and performance.

Findings

Cooperative spectrum sharing improves network performance.

Potential game convergence to Nash equilibrium.

No-regret learning performs well with less overhead.

Abstract

In this work, we propose a game theoretic framework to analyze the behavior of cognitive radios for distributed adaptive channel allocation. We define two different objective functions for the spectrum sharing games, which capture the utility of selfish users and cooperative users, respectively. Based on the utility definition for cooperative users, we show that the channel allocation problem can be formulated as a potential game, and thus converges to a deterministic channel allocation Nash equilibrium point. Alternatively, a no-regret learning implementation is proposed for both scenarios and it is shown to have similar performance with the potential game when cooperation is enforced, but with a higher variability across users. The no-regret learning formulation is particularly useful to accommodate selfish users. Non-cooperative learning games have the advantage of a very low overhead for information exchange in the network. We show that cooperation based spectrum sharing etiquette improves the overall network performance at the expense of an increased overhead required for information exchange.