Research On Collaborative Spectrum Sensing Optimization Algorithm For Maximizing Energy Efficiency

With the rapid development of wireless communication technology in today’s society, people’s demand for spectrum resource more and more urgent considering the social economic conditions and quality of life are increasing, these put forward higher requirements to the efficient and stable wireless communication technology. However, after the new and high wireless communication technologies rise in response to the proper time and conditions, current spectrum allocation policy and principle are still is constant, it allocates the special frequencies to the special user, and a special spectrum is occupied by a particular user alone, other users can not share with it. The Spectrum allocation scheme makes the most of the licensed spectrum has not been effective used, but the current spectrum resources is still short.In order to make this series of problems to be resolved, and fully meet the needs of today’s development, the important cognitive radio technology emerges, it can adequate utilize licensed spectrum and fully meet the current communication needs. This technology improves spectrum efficiency by renting or sharing authorized users’licensed spectrum. As a kind of intelligent wireless communication system, cognitive users can sense their surrounding wireless environment and adapt to it. After the cognitive radio system detects that the authorized channel is an idle channel, the user can communicate with a few interference.This paper investigates the effects of cooperative spectrum sensing (CSS) on energy consumption and achievable performance to maximize the energy of the system efficiency by studying the existing cooperative spectrum sensing method, and proposes the optimization algorithm that optimizing the number of sensing users to get the method for maximizing system throughput and minimizing energy consumption. Then, the paper presents an iterative bisection algorithm for maximizing energy efficiency. Theoretical analysis and simulation results show that using this method to select a number of cognitive users to participate in collaborative spectrum sensing can improve the performance of the system, maximize throughput, at the same time, minimize the energy consumption, as a result, it maximizes the energy efficiency of the system.