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.

随着6G技术的发展，移动数据的增长预计将增加很多倍。控制平面负载（IoT，IoE）也将增加。这样的问题要求能够有效利用资源来优化系统性能的技术，并且可能的解决方案是认知无线电技术。由于频谱感知是认知无线电技术的关键推动力，因此在本文中，定义认知无线电网络（CRN）频谱感知效率的多目标参数，即吞吐量，干扰和能效，用以下术语定义：处理时间，功率分配和检测阈值。在本文中，提出了一种新颖的多目标修正灰狼优化算法（MOMGWO），以解决认知无线网络频谱感知领域中的多目标优化问题，这是无线通信技术的重要范例。灰狼优化（GWO）中的修改用于平衡常规GWO中勘探与开发过程之间的权衡，以获得全局最优。根据领导者选择的变异，区分权重和变异系数来引入修改。将该算法的非支配解集与现有算法进行比较，如非支配排序遗传算法（NSGA-II），多目标粒子群优化（MOPSO），多目标猫群优化（MOCSO），和常规的多目标灰狼优化（MOGWO）算法。仿真结果表明，提出的MOMGWO在帕累托前沿的质量方面优于现有算法。因此，可以通过提出的MOMGWO算法获得在优化认知无线电网络的多目标问题中频谱感测参数的最佳解决方案。