Resource Allocation for Multi-User Cognitive Radio Systems Using Multi-agent Q-Learning

Abstract

Cognitive Radio (CR) is a new generation of wireless communication system that enables unlicensed users to exploit underutilized licensed spectrum to optimize the radio spectrum utilization. The resource allocation is difficult to achieve in a dynamic distributed environment, in which CR users take decisions to select a channel without negotiation, and react to the environmental changes. This paper focuses on using a multi-agent reinforcement-learning (MARL), Q-learning algorithm, on channels selection decision by secondary users in 2×2 and 3×3 cognitive radio system. Numerical results, obtained with MATLAB, demonstrate that resource allocation is realized without any negotiation between secondary and primary users. In this work, the analogy between the numerical and simulated results is also noted.