Multi-Objective Modified Grey Wolf Optimization Algorithm for Efficient Spectrum Sensing in the Cognitive Radio Network
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RESEARCH ARTICLE-COMPUTER ENGINEERING AND COMPUTER SCIENCE
Multi-Objective Modified Grey Wolf Optimization Algorithm for Efficient Spectrum Sensing in the Cognitive Radio Network Geoffrey Eappen1 · T. Shankar1 Received: 30 May 2020 / Accepted: 26 October 2020 © King Fahd University of Petroleum & Minerals 2020
Abstract With the advancement toward 6G technology, mobile data growth is estimated to increase many fold. There will also be an increase in the control plane load (IoT, IoE). Such problems call for the technologies that can efficiently utilize the resources to optimize the system performance, and the possible solution is cognitive radio technology. As the spectrum sensing is the key enabler of cognitive radio technology, in this paper, the multi-objective parameters defining the efficiency of spectrum sensing for a cognitive radio network (CRN), which are throughput, interference, and energy efficiency, defined in terms of sensing time, power allocation, and detection threshold are dealt. In this paper, a novel Multi-Objective Modified Grey Wolf Optimization (MOMGWO) algorithm is proposed to solve the multi-objective optimization problem in the field of spectrum sensing in a cognitive radio network which is an important paradigm in wireless communication technology. Modification in Grey Wolf Optimization (GWO) is applied to balance the trade-off between exploration and exploitation process in conventional GWO, to obtain global optima. Modification is introduced in terms of mutation in leader selection, discrimination weight, and mutation coefficient. The non-dominated solution set of the proposed algorithm is compared with the existing algorithms like Non-dominated Sorting Genetic Algorithm (NSGA-II), Multi-Objective Particle Swarm Optimization (MOPSO), Multi-Objective Cat Swarm Optimization (MOCSO), and conventional Multi-Objective Grey Wolf Optimization (MOGWO) algorithm. The simulation result shows that the proposed MOMGWO has outperformed the existing algorithms with respect to the quality of the Pareto front. Thus, the best solutions for the spectrum sensing parameters in optimizing multi-objective problems for cognitive radio network can be obtained via the proposed MOMGWO algorithm. Keywords Cognitive radio network · Multi-objective optimization · Modified Grey Wolf Optimization · Spectrum sensing
1 Introduction Radio spectrum is an important natural resource having applications in different wireless technologies. Frequency spectrum allocated to technologies like 3G and 4G is heavily crowded, 5G technology facing the issues of network blockage conditions and frequency allocation [1], and on the other hand, the frequency band of TV is largely vacant resulting in spectrum inefficiency. With the increase in the number of mobile devices and the limited availability of the radio spectrum, it is becoming difficult for existing wireless tech-
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T. Shankar [email protected] Geoffrey Eappen [email protected]
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School of Electronics Engineering (SENSE), Vellore Institute of Technology (VIT), Vell
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