Research On Energy Efficiency Optimization In Massive MIMO System

As one of the key technologies in 5G communication,Massive multiple-input multiple-output(MIMO)technology has high diversity gain and spatial gain,which effectively improves channel capacity and system performance and meets users requirements for high data transmission rates.Therefore,it has attracted wide attention and study.However,the radio frequency link of the massive MIMO system has produced a large energy consumption,so how to improve the energy efficiency of the massive MIMO system has become one of the urgent problems to be studied.This thesis studies the resource allocation problem and hardware adjustment in the energy efficiency of massive MIMO systems.Previous studies have considered a single factor that affects the energy efficiency of massive MIMO systems.This thesis proposes to study the impact of three variables on energy efficiency.And on this basis,this thesis further studies the energy efficiency of massive MIMO systems equipped with low-precision analog-to-digital converter(ADC).The main research contents of the thesis are as follows:Chapter 1 mainly introduces the research background of this thesis,introduces the related concepts of 5G massive MIMO,and analyzes the related research on spectrum efficiency and energy efficiency.Chapter 2 introduces the classification and model of wireless channel fading,the channel model and capacity of MIMO system,and the key technologies in Massive MIMO,then gives a clear definition of energy efficiency and common methods in energy efficiency optimization.Chapter 3 studies the Massive MIMO uplink system with Zero-Forcing(ZF)reception,and proposes a jointly base station antenna number,transmit power and user set three-variable optimization scheme.Under the premise of limiting single-user transmit power and data transmit rate,an energy efficiency function related to the number of antennas,transmit power and user set is established,and the three variables are optimized with the goal of maximizing energy efficiency.Because the three variables are related to each other,it is difficult to directly optimize.Therefore,it is proposed to convert the three-variable joint optimization problem into two sub-optimization problems.First,use the fractional convex optimization theory to derive the optimal expressions for the number of antennas and the transmit power.Based on this,the idea of the water-filling method is introduced to give priority to users with better channel conditions,thereby optimizing the user set.Finally,the Dinkelbach algorithm is used to jointly iterate the three variables to obtain the variable value that maximizes energy efficiency.Experimental results prove that the proposed algorithm can reduce the transmission power and improve energy efficiency performance and spectrum efficiency.Chapter 4,on the basis of updating the power of a single user in Chapter 3,further proposes to equip the base station with low-precision ADCs,which reduce system power consumption.However,low-precision ADCs will have a certain impact on the performance of the system due to the low quantization accuracy.Therefore,how to weigh the accuracy value and system performance is the key research content of this chapter.This thesis studies the massive MIMO uplink transmission system with Maximum-Ratio Combining(MRC)reception,which aims at maximizing energy efficiency and establishes an energy efficiency function with the number of antennas,single user transmit power,and ADC quantization accuracy.First,the fractional programming theory and Lagrangian function are used to derive the optimal expressions for the number of base station antennas and single user transmit power,and then the Dinkelbach algorithm is used to combine the number of base station antennas and single user transmit power to iterate the optimal quantization accuracy value.Experimental results show that the proposed algorithm guarantees high data transmission rate and improve energy efficiency performance.Chapter 5 is the summary and outlook.Summarize the research content of this thesis,and make further prospects for future research work.