An Optimized Hybrid Approach for Spectrum Handoff in Cognitive Radio Networks With Non-Identical Channels

Abstract

Cognitive radio (CR) is a technology that aims to enhance the use of the underutilized spectrum. Spectrum handoff plays this role by allowing the unlicensed secondary users (SUs) to use the licensed frequency bands of the primary users (PUs). Moreover, it helps the SU to vacate the channel at the presence of the PU and find a suitable channel for the interrupted SU to continue his unfinished transmission. In this paper, an analytical model for the general case of non-identical channels in CR networks is introduced for both fixed and probabilistic sequence approaches for target channel selection. Moreover, a new balancing model is suggested to generate the probabilistic sequence. Both the fixed and probabilistic sequence approaches are optimized using the meta-heuristic methods of particle swarm and genetic algorithm in the seek of finding an optimal approach to minimize the extended data delivery time of the SUs. The results are based on the pre-emptive resume priority M/M/1 queueing network model and are shown for different network cases. Based on the optimization results, a fast hybrid approach is proposed and found to achieve near optimal solution.