Multi-agent Q-Learning of Channel Selection in Multi-user Cognitive Radio Systems: A Two by Two Case

TL;DR

This paper proposes a multi-agent Q-learning approach for channel selection in cognitive radio systems, enabling secondary users to learn optimal channels without negotiation, thus reducing overhead and avoiding collisions.

Contribution

It introduces a negotiation-free channel selection scheme using multi-agent Q-learning with proven convergence, applicable to multi-user cognitive radio environments.

Findings

Q-learning converges to optimal strategies.

The scheme reduces negotiation overhead.

Numerical simulations demonstrate effective learning.

Abstract

Resource allocation is an important issue in cognitive radio systems. It can be done by carrying out negotiation among secondary users. However, significant overhead may be incurred by the negotiation since the negotiation needs to be done frequently due to the rapid change of primary users' activity. In this paper, a channel selection scheme without negotiation is considered for multi-user and multi-channel cognitive radio systems. To avoid collision incurred by non-coordination, each user secondary learns how to select channels according to its experience. Multi-agent reinforcement leaning (MARL) is applied in the framework of Q-learning by considering the opponent secondary users as a part of the environment. The dynamics of the Q-learning are illustrated using Metrick-Polak plot. A rigorous proof of the convergence of Q-learning is provided via the similarity between the Q-learning and Robinson-Monro algorithm, as well as the analysis of convergence of the corresponding ordinary differential equation (via Lyapunov function). Examples are illustrated and the performance of learning is evaluated by numerical simulations.