Research On Energy Efficiency Optimization In Massive MIMO Systems

Massive MIMO is an important technology in the fifth generation mobile communication system.Compared with the traditional multi-user MIMO system,Massive MIMO greatly improves the performance of systems by configuring a large number of transmission antennas at the base station.However,the increase in the number of antennas also leads to more power consumption.At this time,the energy consumed by the global wireless communication systems is showing a trend of sharp increase,and the green communication with the goal of energy efficiency optimization has become an important direction of wireless communication research.Therefore,this thesis studies the energy efficiency optimization in Massive MIMO systems,the main research contents of this thesis are as follows:1.Aiming at the problem of optimizing energy efficiency through power allocation in Massive MIMO,this thesis proposes a power allocation algorithm based on Glowworm Swarm Optimization.This algorithm optimizes the search process of the power allocation scheme by simulating the interactive behavior of the glowworm swarm,and has the advantages of high search accuracy,fast convergence speed and few adjustment parameters.The proposed algorithm is compared with other typical algorithms and the simulation results show that the proposed algorithm can reduce complexity while ensuring performance,which verifies its effectiveness.2.In order to balance energy efficiency and spectrum efficiency in Massive MIMO systems,this thesis proposes an algorithm based on differential evolution to solve the joint optimization problem of energy efficiency and spectrum efficiency with two decision variables,total transmit power and number of transmit antennas.This proposed algorithm is also compared with other typical algorithms and the simulation results show that the proposed algorithm can guarantee the diversity and extensiveness of the Pareto optimal solution set and then obtain the optimal transmission power and the number of antennas of the system under different maximum energy efficiency requirements with low computational complexity.