Performance Evaluation of Route Selection Schemes over a Clustered Cognitive Radio Network

Abstract

Cognitive radio (CR) is a promising next-generation wireless communication system that provides efficient utilization of radio spectrum by enabling unlicensed users (or secondary users, SUs) to sense for and use underutilized radio spectrum (or white spaces) owned by licensed users (or primary users, PUs). In this paper, we investigate the effects of a larger network size (or higher number of routes) on non-clustered, clustered and clustered-reinforcement learning (RL)-based route selection schemes in a USRP/ GNU radio platform focusing on the network layer. Experimental results show that the enhanced variant of reinforcement learning (RL)-based route selection scheme (C-ERL) selects stable route(s) over a clustered CRN in a USRP/ GNU radio platform. C-ERL improves cluster stability by reducing the number of route breakages caused by route switches, and network scalability by reducing the number of clusters in the network without significant deterioration of QoS, including throughput, packet delivery rate, and end-to-end delay.