MBSO Algorithm For Handling Energy-Throughput Trade-Off In Cognitive Radio Networks

Abstract

Abstract Cognitive Radio network (CRN) depends on opportunistic spectrum access and spectrum sensing for improving the wireless networks’ spectrum efficiency. Since throughput maximization can result in high-energy consumption, the spectrum sensing technique should address the energy-throughput trade-off. The spectrum sensing time has to be determined by the considering the residual battery energies of each secondary user (SU). The primary user (PU) interference degrades the throughput of the entire network. Hence, the transmit power level should be determined by considering the PU interference and SU battery energy. This paper proposes the multi-objective brain storm optimization (MBSO) algorithm for handling energy-throughput trade-off in CRN. In this work, the sensing time is adaptively determined based on the residual battery energy of SUs, and the transmit power is determined based on the energy level of the PU signal and the residual battery energy of the SUs. A multi-objective optimization problem is formulated in order to maximize the throughput and minimize the packet error rate (PER) and is solved by applying the MBSO algorithm. Experimental results show that MBSO attains improved throughput, higher residual energy with lesser PER. The spectrum sensing performance is enhanced with higher probability of detection.