Research On Cooperative Information And Energy Optimization Algorithms

With the fifth generation(5 G)becoming more and more mature,new business and upgraded user demands bring the communication system more traffic and energy consumption.At the same time,it is easier to obtain information with the development of the Internet.Thus,the cooperation of information and energy will be the trend of future development.Simultaneous wireless information and power transfer(SWIPT)is a case of cooperative information and energy.In SWIPT,security is an important problem,and the energy efficiency is usually sacrificed to ensure security,leading to the decrease of system performance.In the ultra dense network(UDN),massive base stations will lead to huge backhaul overhead and energy consumption.To solve these problems,this thesis proposes two cooperative information and energy optimization algorithms.The main works and innovations are as follows:Firstly,a cooperative information and energy optimization algorithm with constrained energy efficiency in SWIPT is proposed.Based on the secrecy rate and energy harvest,the system energy efficiency of secure SWIPT is defined,which considers the potential security risks and the equipment energy.To ensure the system energy efficiency,the receiving ratio of information and energy,beam-forming and artificial noise are optimized.An optimization problem is constructed with transmission power as the optimization goal and the system energy efficiency as one one the constraints.The optimal beam-forming,artificial noise and receiving ratio of energy and information are obtained by semi-definite relaxation(SDR)and convex approximation.Simulation results show that the proposed algorithm can guarantee the energy efficiency of the secure S WIPT system,and the transmission power is less than the contrast algorithms.Secondly,a cooperative information and energy optimization algorithm with social relationship in UDN is proposed.The demand similarity between users is analyzed based on social information,and the optimal user association is obtained.Then the minimum backhaul traffic is realized.At the same time,based on the energy harvest,the energy trading strategy is adopted to reduce the energy cost of the base station.To minimize the tiraffic overhead and the energy trading cost,the optimization problem is built.The optimal user association and energy trading strategy are obtained based on the matching game and the convex optimization toolbox.Simulation results show that the proposed algorithm performs better than comparison algorithms in backhaul traffic overhead and the base station energy cost.