Optimization techniques for spectrum handoff in cognitive radio networks using cluster based cooperative spectrum sensing

Spectrum handoff has an undesirable effect in utilizing the space for Secondary user (SU) in the spectrum, which causes a handoff delay in cognitive radio network. The SU frequently faces the problem of handoff process which is likely to interrupt the service and substantial delay over the quality of service during the transmission. It struggles towards identifying the channel during the handoff by occupying a major role in today's era. Based on this research, an effectual spectrum handoff scheme is anticipated using Spectrum Binary Particle Swarm Optimization (SpecBPSO) algorithm and M/G/1 queuing model. Towards improving the efficiency of SU and reducing the congestion over channel, Cluster Based Cooperative Spectrum Sensing (CBCSS) is used. The cluster head is selected dynamically based on the sensing signal of the SU. The cluster head is associated with the SU base station to report the active and inactive channel in the spectrum and later decision report is generated by the fusion center. In this proposed method, SpecBPSO uses the Boolean variable to reduce the total service time for handoff to find the optimal global value using bitwise and mutation operator format. This study work also presents an outline to observe the outcome of primary user's activity and the delay performance of spectrum handoff with the possible interruptions in a CR network. The simulation setup of the proposed work is compared with spectrum particle swarm optimization (SpecPSO), binary particle swarm optimization (BPSO) and ant colony optimization that provide a better tradeoff over the delay achievement, maximize the SNR with the three benchmark functions and optimal handoff.

Automated summary

This study aims to design an effective spectrum handoff scheme using SpecBPSO algorithm and M/G/1 queuing model, and enhance the efficiency of SU by using Cluster Based Cooperative Spectrum Sensing. By dynamically selecting cluster heads based on SU sensing signals and associating them with SU base stations, better reporting of active and inactive channels in the spectrum can be achieved.