A Q-learning-based dynamic channel assignment technique for mobile communication systems

This paper deals with the problem of channel assignment in mobile communication systems. In particular, we propose an alternative approach to solving the dynamic channel assignment (DCA) problem through a form of real-time reinforcement learning known as Q learning. Instead of relying on a known teacher, the system is designed to learn an optimal assignment policy by directly interacting with the mobile communication environment. The performance of the Q-learning-based DCA was examined by extensive simulation studies on a 49-cell mobile communication system under various conditions including homogeneous and inhomogeneous traffic distributions, time-varying traffic patterns, and channel failures. Comparative studies with the fixed channel assignment (FCA) scheme and one of the best dynamic channel assignment strategies (MAXAVAIL) have revealed that the proposed approach is able to perform better than the FCA in various situations and is capable of achieving a similar performance to that achieved by MAXAVAIL, but with a significantly reduced computational complexity.