Energy Efficiency And Spectral Efficiency Trade-Off In Massive MIMO System

Compared with the traditional multi user MIMO system, the Massive MIMO system can meet the requirements of the fifth generation mobile communication by increasing the number of transmit antennas. However, in consideration of the power consumption of the circuits, large number of the transmit antennas means more radio frequency units and more energy consumption, which leads to the decrease of the energy efficiency of the system. This leads to the contradiction between efficiency and energy efficiency of the system. In this paper, the spectral efficiency and energy efficiency of the system can be optimized by selection of the transmit antennas and appropriate transmit power reasonably.The main work of this paper is as follows:Mathematical model of the system in which large scale fading and small scale fading are considered for both zero forcing and maximum ratio transmit precoding massive MIMO system is established. Under this model, the relationship between spectral and energy efficiency is investigated.With the concept of multi-objective optimization, spectral and energy efficiency trade-off is analyzed by Pareto optimality. The working points corresponding to the optimal relation of Pareto which control the state of the system are discussed.Including direct particle swarm algorithm, particle swarm algorithm based on weighted sum, particle swarm algorithm based on NBI, three algorithms are presented for solving the Pareto optimal relation and work points. These three algorithms have their own characteristics. The direct particle swarm algorithm is easy to be understood for its idea is direct from Pareto concept, but it is difficult to get work points. The particle swarm algorithm based on weighted sum is simple, but the solutions are not uniform. And particle swarm algorithm based on NBI is the best of the three algorithms, and can find the uniform solutions with any interval.The effect of the users is considered. The influence of the number of users and the power allocation to the efficiency and energy efficiency of the system are analyzed. Through the power allocation between the user, the Pareto optimization curve has a certain improvement.