Reinforcement Learning Based Handoff Mechanism in Cooperative Cognitive Radio Networks

Abstract

The spectrum handoff (SH) is a dynamic spectrum access technique which ensures effective channel utilization, fair resource allocation, as well as uninterrupted real-time connection. Facilitating SH across traffics of dissimilar characteristics in Cognitive Radio Networks (CRNs) is posing difficulty due to manifold interventions from Primary Users (PUs), disagreement among Secondary Users (SUs) and diversified Quality of Experience (QoE) demand. Here, we consider effective channel selection strategy (CSS) and put forward a learning-based handoff scheme to enhance QoE demand of users by the introduction of docition idea. A PU prioritized Markov method is introduced to represent the communications between PUs and SUs for even channel access. The reinforcement learning (RL) is applied to CSS to carry out proper channel selection. Numerical outcomes projects that proposed queuing model, suggested learning based handoff scheme and docitive learning enhances the quality of service by maintaining the average MOS of 3.6.