Implementation of Enhanced Chimp Optimization Algorithm in Cognitive Radio Networks for Vehicular Mobile Communication

In recent years, 6G technology has been extended to many different applications, especially mobile communications. As a result, the volume of mobile data increases, which poses a problem with the load on the plane of control (IoE, IoT). This problem is solved by efficient use of resources and reduced power consumption in cognitive radio networks (CRNs). In the literature, many methods have been developed by researchers to control spectrum sensing as well as energy -efficient operation, but they still need to be improved to improve system efficiency and processing power. Therefore, in this paper, an energy efficient method for Opposition Function -based Chimpanzee Optimization Algorithm (OFCOA) is developed in CRN for energy management as well as resource allocation. The proposed method is a combination of Opposition Function (OF) and Chimpanzee Optimization Algorithm (COA). In COAs, the optimal decision process is enhanced by the use of OF. The proposed method provides energy efficient operation in CRN through energy management taking into account spectrum measurements. The proposed method was tested under four Primary User (PU) and Secondary User (SU) conditions with channel occupation and CRN findings. The proposed methodology is implemented in MATLAB and performance is measured based on performance metrics such as processing power, network life, transmission rate, delay, flush, power consumption and overhead. The performance of the proposed methodology is compared with traditional methods such as Chimpanzee Optimization Algorithm (COA), Whale Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO).