Context-awareness and intelligence in distributed cognitive radio networks: a reinforcement learning approach

Cognitive Radio (CR) is a next-generation wireless communication system that exploits underutilized licensed spectrum to improve the utilization of the overall radio spectrum. A Distributed Cognitive Radio Network (DCRN) is a distributed wireless network established by a number of CR hosts in the absence of fixed network infrastructure. Context-awareness and intelligence are key characteristics of CR networks that enable the CR hosts to be aware of their operating environment in order to achieve a joint action that improves network-wide performance in a distributed manner through learning. In this paper, we advocate the use of Reinforcement Learning (RL) in application schemes that require context-awareness and intelligence such as the Dynamic Channel Selection (DCS), scheduling, and congestion control. We investigate the performance of the RL in respect to DCS. We show that RL and our enhanced RL approach are able to converge to a joint action that provide better network-wide performance. We also show the effects of network density and various essential parameters in RL on the performance