Research On Energy Efficiency Optimization Of Compact Massive MIMO Systems

Massive Multiple-Input Multiple-Output(MIMO)technology have gradually evolved into potential technology for the B5G/6G system,and the hope of achieving higher capacity gains will remain in the spatial dimension.However,in actual engineering practice,massive MIMO systems still face challenges.Firstly,as the number of antennas increases,the large number of radio frequency(RF)chains in the base station will lead to high energy consumption and cost.This makes wireless systems face the challenge of energy-efficient transmission while satisfying the unprecedented demand for higher data rates.Secondly,for some applications that require smaller physical area(windward area),the array deployment tends to be compact.In this case,the energy consumption problem is further aggravated,and the proximity of the antenna elements as electrical components causes antenna mutual coupling(MC).Therefore,this thesis presents a general energy-efficient beamforming(EEBF)optimization scheme for the Massive MIMO system.Then,by modeling MC of the compact Massive MIMO systems,the energy efficiency optimization solutions are studied in the presence of MC.The main contributions are listed as follows:(1)For the challenge of massive MIMO systems to meet the higher data rates requirement while achieving lower energy consumption,the beamforming optimization for the downlink of single cell and multi-users massive MIMO systems is studied.First,a theoretical analysis and derivation are made on the sum rate and the overall power consumption model of the system.Then,a mathematical model for maximizing energy efficiency is established,which satisfies the constraints of transmit power and quality of service.In order to solve this nonconvex and nonlinear fractional programming,an energy-efficient beamforming(EE-BF)iterative algorithm is proposed by introducing multiple auxiliary variables and the successive parametric convex approximation theory(SPCA).Compared to existing two-layer iterative methods based on Dinklebach's theory,the proposed approach uses only one loop with a small number of iterations aiming to yield a Karush-Kuhn-Tucker(KKT)point.The simulation results show that the proposed approach is a fast convergence algorithm,and it is more energy-efficient than traditional algorithms in the case of low power consumption.(2)In view of the MC effects on the compact Massive MIMO systems and the problem of high energy consumption and cost caused by the large number of RF links,the antenna selection(AS)technology is introduced into the compact Massive MIMO systems.Then,the sum rate and total power consumption of the systems are theoretically modeled,including the circuit power consumption of the RF chains,antenna selection switch control and so on.With the goal of maximizing energy efficiency,the antenna subset and beamforming are jointly optimized.The key is to introduce auxiliary variables,solve the coupling between the two optimization variables through perspective reformulation,and propose a sub-optimal iterative(EE-BF&AS)algorithm based on the sequential parameter convex approximation theory.At last,according to the equivalent circuit method,we model the antenna MC to simulate the performance of the proposed EE-BF&AS algorithm.The numerical results show that when decreasing interval between the antennas,MC increases,resulting in a reduction in EE.By the AS strategy,the irregular antenna subset is obtained,which can make MC less serious and reduce the power consumption of RF chains,so the EE is further enhanced.