Research On Energy Efficiency Optimization Of Massive MIMO System

The rapid development of wireless communication technology has driven the exponential growth of wireless broadband service volume and transmission rate,making the existing 4G technology unable to meet the demand for future communication in terms of transmission rate and resource utilization.In addition,the dramatic increase in the number of users and the scale of the network has brought about a significant increase in energy consumption.Therefore,green communication with the goal of maximizing energy efficiency has become an important research direction for 5G communication.Among many key technologies of 5G,massive MIMO technology has become one of the key technologies of 5G with its advantages of ultra-high spectrum efficiency and good channel propagation conditions.The massive MIMO technology is equipped with large number of antennas,and combined with the space division multiplexing technology,it can effectively improve the spectral efficiency,energy efficiency and link reliability of the system without additional bandwidth and transmission power.In summary,it is of great theoretical and practical significance to study the energy efficiency optimization of massive MIMO systems.Therefore,the main research contents of this paper are as follows.(1)The energy efficiency optimization problem of the uplink of a massive MIMO system with non-ideal CSI is studied.The data transmission rate of each user is derived taking into account the channel estimation error,an energy efficiency optimization model is established,and an adaptive particle swarm optimization-based power allocation algorithm is proposed.The algorithm optimizes the user transmit power allocation by simulating the random motion of particles in space,and combining the information sharing between the particles themselves and the whole particle swarm,with the advantages of high search accuracy,fast convergence and few adjustable parameters.The proposed algorithm is compared with two other classical algorithms through simulation,and the results show that the proposed algorithm converges quickly and obtains the highest energy efficiency value,which verifies the effectiveness and feasibility of the algorithm.(2)The energy efficiency optimization problem of massive MIMO downlink system is studied.Assuming that the perfect CSI is known at both the base station and the user,we derive the lower bound closed expression for the achievable downlink rate based on zero forcing and max ratio precoding.Combined with the power consumption model,we derive a nonconvex optimization problem about transmit power and the number of antennas.Due to the coupling relationship between the optimization variables,it is very difficult to solve the original problem directly.Based on the convex optimization theory and the Lagrangian dual method,we propose a joint antenna selection and user power allocation scheme.Through the alternating iteration between the number of antennas and power,the optimal number of antennas and the optimal power vector are solved.The simulation results show that the proposed iterative algorithm is effective,which can significantly improve the energy efficiency.