Energy aware resource allocation and complexity reduction approach for cognitive radio networks using game theory

Nowadays, the demand for mobile wireless communication systems has increased drastically due to its significant use for various real-time applications. This increased demand for communication causes heavy utilization of the radio spectrum to improve ubiquitous computing services. However, systems providing high-speed communication fail to achieve the desired performance due to unsystematic spectrum utilization and resources. The problem addressed by Cognitive Radio Network (CRN) architecture has attracted research and industrial community to enhance the real-time communication systems. Although CRN based real-time communication systems suffer from resource allocation, spectrum sensing, and power consumption issues. In this paper, we introduce a novel approach for resource allocation and sharing based on cooperative game theory, and cooperative node selection ensures maximized payoff. The proposed method optimizes the overhead, energy consumption, and resource utilization. Further, energy consumption and resource allocation issues transformed into an optimization problem. A backtracking search algorithm is applied to reduce the computation complexity and to find the optimal solution for resource utilization. The simulation result obtained achieves better performance compared to the existing energy-aware scheduling approach in CRN. (C) 2020 Elsevier B.V. All rights reserved.