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

MBSO Algorithm For Handling Energy-Throughput Trade-Off In Cognitive Radio Networks M Ramchandran Department of ECE , , Krishnapuram, Pallavaram Vels University , , Krishnapuram, Pallavaram E N Ganesh Dean Engineering and Technology , Vels University, Krishnapuram, Pallavaram 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.
07 15 2022 07 15 2022 05 28 2021 1717 1725 https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model 10.1093/comjnl/bxab012 https://academic.oup.com/comjnl/article/65/7/1717/6287333 https://academic.oup.com/comjnl/article-pdf/65/7/1717/44921918/bxab012.pdf https://academic.oup.com/comjnl/article-pdf/65/7/1717/44921918/bxab012.pdf Wireless Communications and Mobile Computing Salah 2018 Wireless Communications and Mobile Computing Wang 2018 EURASIP J. Wirel. Commun. Netw. Wu 13 2–14 2018 Energy-efficiency opportunistic spectrum allocation in cognitive wireless sensor network Sensing Throughput Tradeoff for Cognitive Radio Networks with Noise Variance Uncertainty Bogale 2014 10.4108/icst.crowncom.2014.255811 J. Comput. Netw. Commun. Al-Doseri 2016 1–9 2016 Performance analysis of cooperative spectrum sensing under guaranteed throughput constraints for cognitive radio networks Int. J. Distrib. Sens. Netw. Khalid 13 24–31 2017 10.1177/1550147717744992 Optimal sensing performance for cooperative and non-cooperative cognitive radio networks Appl. Sci. Sana Ullah Jan 8 242–254 2018 Throughput maximization using an SVM for multi-class hypothesis-based spectrum sensing in cognitive radio Information Yawada 7 45–52 2018 Tradeoff analysis between spectral and energy efficiency based on sub-channel activity index in wireless cognitive radio networks 2013 2nd IEEE/CIC International Conference on Communications in China (ICCC), China: Wireless Networking and Applications Zhang 2013 IEEE Trans. Wirel. Commun. Xu 2017 10.1109/TWC.2017.2684125 End-to-end throughput maximization for underlay multi-hop cognitive radio networks with RF energy harvesting IEEE Open Access Xu 7 4338–4387 2019 Multi-hop cognitive wireless powered networks: Outage analysis and optimization Comput. J. Zhang 60 6 2017 10.1093/comjnl/bxx024 Throughput optimization for energy harvesting cognitive radio networks with save-then-transmit protocol Wireless Personal Communications Nguyen 112 1783–1800 2020 Energy harvesting for throughput enhancement of cooperative wireless sensor networks IEEE Access Bouabdellah 7 173329–173343 2019 10.1109/ACCESS.2019.2955205 Cooperative energy harvesting cognitive radio networks with spectrum sharing and security constraints Int. J. Swarm Intell. Res Shi 2 35 2011 10.4018/ijsir.2011100103 An optimization algorithm based on brainstorming process Advances in Swarm and Computational Intelligence, Lecture Notes in Computer Science Guo 365 2015 Int. J. Swarm Intell. Res. Shi 4 1 2013 10.4018/ijsir.2013070101 Multi-objective optimization based on brain storm optimization algorithm Conference Paper in Lecture Notes in Computer Science Xue 513 2012 Complexity Dai 11 1–11 2019 A multiobjective brain storm optimization algorithm based on decomposition