Cognitive radio networks for delay-sensitive applications: Games and learning

Abstract

We have witnessed an explosion in wireless video traffic in recent years. Video applications are bandwidth intensive and delay sensitive and hence require efficient utilization of spectrum resources. Born to utilize wireless spectrum more efficiently, cognitive radio networks are promising candidates for deployment of wireless video applications. In this chapter, we introduce our recent advances in foresighted resource allocation mechanisms that enable multiuser wireless video applications over cognitive radio networks. The introduced resource allocation mechanisms are foresighted, in the sense that they optimize the long-term video quality of the wireless users. Due to the temporal coupling of delay-sensitive video applications, such foresighted mechanisms outperform mechanisms that maximize the short-term video quality. Moreover, the introduced resource allocation mechanisms allow wireless users to optimize while learning the unknown dynamics in the environment (e.g., incoming traffic, primary user activities). Finally, we introduce variations of the mechanisms that are suitable for networks with self-interested users. These mechanisms ensure efficient video resource allocation even when the users are self-interested and aim to maximize their individual video quality. The foresighted resource allocation mechanisms introduced in this chapter are built upon our theoretical advances in multiuser Markov decision processes, reinforcement learning, and dynamic mechanism design.