Certain investigations on performance analysis of energy efficient techniques for cognitive radio networks

Abstract

In recent years, the 5G technology has been increased in many different applications especially in mobile communications. So, the mobile data growth is increased which creates the issues in the control plane load (IoE, IoT). These problems are solved by efficient utilization of the resources and reduce the energy consumption in the Cognitive Radio Network (CRN). In literature, many methods are developed by researchers to manage the spectrum sensing and energy efficient operation, still require the improvement to increase the system efficiency and capacity. Hence, in this research, hybrid Whale Optimization Algorithm (WOA) with Firefly Algorithm (FA) is proposed to attain the energy efficiency deliberation. This method is intended for properly maintaining objective signal to interference plus noise ratio (SINR) need for the received interferences and cognitive users at receiver of primary under a reference value. While utilizing the WOA, it has slow convergence and it is easy to fall on local optimum solution. So, to avoid the issues, recent optimization algorithm is developed named as Oppositional Function Based Chimp Optimization Algorithm (OBCOA) energy efficient method. This method is developed in the CRN to manage the energy as well as resource allocation. In the first work, hybrid WOA with FA is analyzed and the second work, OBCOA is utilized to analyze the energy efficient in CRN system. The proposed method is enabling energy efficient operation in CRN by accomplish the energy with the consideration of spectrum sensing. The proposed method is validated with the consideration of four conditions of Primary User (PU) and Secondary User (SU) with channel occupation and detection in CRN network. The proposed methodology is implemented in MATLAB and iv performances are evaluated by performance metrices such as throughput, network life time, delivery ratio, delay, drop, energy consumption and overhead. The performance of the proposed methodology is compared with the conventional methods such as Chimp Optimization