Wireless communication technology is used in various applications and therefore the availability of wireless spectrum is a serious concern. The number of cellular users is increasing rapidly. The 5G network will be able to cater to the requirements of the increasing users. However, the spectrum efficiency needs to be improved. Cooperative spectrum sensing is being widely used by cognitive radios for utilizing the available spectrum in an efficient manner. Evolutionary Algorithm based optimization methods are used in various applications and have proved to be very efficient. These algorithms can also be used for optimizing the cooperative spectrum sensing in cognitive radios. In this paper, two methods are proposed for optimal Cooperative Spectrum Sensing for 5G cognitive networks. The optimization algorithms are designed using whale optimization algorithm (WOA) and Particle Swarm Optimization (PSO). The objective is to increase the probability of detection by optimizing the ‘weighting vector’. In the first method, WOA is used for cooperative spectrum sensing optimization in cognitive radios. In the second method, WOA method is improved using the PSO algorithm. A hybridized WOA-PSO algorithm is proposed to further improve the probability of detection. The results obtained are compared with other existing algorithms. The proposed methods perform better than the existing methods.

无线通信技术被用于各种应用中，因此无线频谱的可用性是一个严重的问题。蜂窝用户的数量正在迅速增加。5G网络将能够满足不断增长的用户的需求。但是，频谱效率需要提高。认知无线电广泛使用合作频谱感测，以有效地利用可用频谱。基于进化算法的优化方法被用于各种应用中，并被证明是非常有效的。这些算法还可用于优化认知无线电中的协作频谱感测。本文提出了两种用于5G认知网络的最佳合作频谱感知的方法。该优化算法是使用鲸鱼优化算法（WOA）和粒子群优化算法（PSO）设计的。目的是通过优化“加权向量”来增加检测的可能性。在第一种方法中，WOA用于认知无线电中的协作频谱感知优化。在第二种方法中，使用PSO算法对WOA方法进行了改进。为了进一步提高检测概率，提出了一种混合式的WOA-PSO算法。将获得的结果与其他现有算法进行比较。所提出的方法比现有方法具有更好的性能。在第二种方法中，使用PSO算法对WOA方法进行了改进。为了进一步提高检测概率，提出了一种混合式的WOA-PSO算法。将获得的结果与其他现有算法进行比较。所提出的方法比现有方法具有更好的性能。在第二种方法中，使用PSO算法对WOA方法进行了改进。为了进一步提高检测概率，提出了一种混合式的WOA-PSO算法。将获得的结果与其他现有算法进行比较。所提出的方法比现有方法具有更好的性能。