Research On Energy Efficiency Optimization For Downlink In Massive MIMO Systems

As society’s demand for communication networks continues to grow,the resource consumption and greenhouse gas emissions brought about by mobile communications have become issues that cannot be ignored in green communications.Therefore,the requirements of energy efficiency performance indicators are clearly put forward in the concept of the sixth generation mobile communication network,and energy efficiency optimization has become a hot research topic in green communication.In order to make Massive Multiple-input Multiple-output(Massive MIMO)technology more in line with the concept of green communication,it is very important to improve the energy efficiency performance indicators of Massive MIMO systems.Therefore,this paper studies the energy efficiency optimization of massive MIMO downlink.The main research work of this paper is as follows:(1)For the downlink energy efficiency optimization of base station transmit power in a single-cell massive MIMO system with perfect Channel State Information(CSI)based on Zero Forcing(ZF)precoding scheme,considering the user’s quality of service(Qo S)fairness The mathematical expression between the system energy efficiency and the base station transmit power is derived,and finally,the closed expression of the optimal transmit power for the given number of antennas and users is obtained according to the Lambertian function.The simulation analysis obtains the optimal system design of energy efficiency under the set scenario and then compares the system parameter design of the optimal system energy efficiency under different power consumption models.The simulation shows that the nonlinear power consumption model is more practical.(2)The energy efficiency optimization problem of power allocation for multi-cell massive MIMO downlink systems with channel space correlation under imperfect CSI is studied,and two power allocation algorithms are proposed: suboptimal power allocation algorithm and swarm intelligence search algorithm.The suboptimal power allocation algorithm first approximates the non-convex optimization objective as the concave-convex fractional problems(CCPF)problem through preprocessing,and finally obtains the closed-form suboptimal solution of the original problem by combining the Dinkelbach algorithm.The simulation results show that the power allocation algorithm is superior to the general classical power allocation algorithm.In order to fully consider the coupling between user optimization variables,the improved swarm intelligence sparrow search optimization algorithm is used to numerically solve the energy efficiency optimization objective.algorithm.23 pictures,2 tables,65 references.