Subcarrier Assignment Schemes Based on Q-Learning in Wideband Cognitive Radio Networks

TL;DR

This paper introduces Q-learning-based subcarrier assignment schemes for wideband cognitive radio networks, addressing dynamic spectrum access challenges with independent and collaborative approaches.

Contribution

It proposes novel independent and collaborative Q-learning schemes for subcarrier assignment in wideband CR networks, improving performance without requiring prior spectrum knowledge.

Findings

Collaborative Q-learning outperforms independent Q-learning in assignment accuracy.

The proposed schemes adapt effectively to dynamic spectrum environments.

Performance gains come with increased computational cost.

Abstract

Subcarrier assignment is of crucial importance in wideband cognitive radio (CR) networks. In order to tackle the challenge that the traditional optimization-based methods are inappropriate in the dynamic spectrum access environment, an independent Q-learning-based scheme is proposed for the case that the secondary users (SUs) cannot exchange information while a collaborative Q-learning-based scheme is proposed for the case that information can be exchange among SUs. Simulation results show that the performance achieved with the proposed collaborative Q-learning-based assignment is better than that obtained with the proposed independent Q-learning-based assignment at the cost of the computation cost.