Research On Key Technologies Of Successive Positioning Information Aided 5G Massive MIMO

Massive Multiple Input Multiple Output(Massive MIMO)and millimeter-wave(mm Wave)communications are considered as key technologies for fifth-generation(5G)communication networks.Due to the richer spectrum resources provided by the millimeter-wave band and higher multiplexing gain provided by massive MIMO systems,the integration of two technologies makes a qualitative leap in the transmission rate and system capacity of the communication network.However,in the 5G scenario,the complex and changeable millimeterwave channel puts forward new requirements for pilot overhead,accuracy and timeliness of channel estimation.As another key technology in MIMO systems,hybrid beamforming can further improve 5G capacity by utilizing spatial resources to compensate for the higher path loss of mm Wave channels.Nonetheless,the traditional beamforming algorithms fail to efficiently keep a balance between the hardware complexity and beamforming gains.Therefore,how to efficiently and timely obtain the rapidly changing channel state information and realize hybrid beamforming is the main research object of this thesis.This thesis studies channel reconstruction technology and hybrid beamforming technology with the assistance of 5G high-precision positioning technology,aiming to use successive and accurate 5G positioning information to realize low-latency and high-precision mm Wave MIMO communication.Specifically,to achieve this goal,this thesis researches two schemes:reconstruct the mm Wave channel state based on the location information and environmental information of the communication terminal in dynamic scenarios and a low-complexity hybrid beamforming algorithm is implemented with the assistance of bidirectional positioning information.The main contributions of this thesis can be summarized as follows:(1)This thesis studies the theoretical basis of 5G massive MIMO system,elaborates the system model,key technologies and channel characteristics of 5G massive MIMO,summarizes the challenges of existing channel estimation and hybrid beamforming technologies,and analyzes the potential of 5G successive high-precision positioning technology to improve the performance of communication systems.(2)This thesis studies a scheme to reasonably reconstruct the communication channel of mobile terminals using high-precision positioning information and Kalman filtering in dy-namic scenarios.Simulation results show that this scheme is superior to traditional channel estimation schemes in terms of bit error rate and transmission efficiency.(3)This thesis proposes a new scheme to decouple the optimal problem of traditional hybrid beamforming as two phases.In the analog beamforming phase,the dominated path among the multi-path components is determined by the transmitter and receiver.In addition,the codebook-based beamforming weight vectors are bidirectionally and synchronously determined according to the angle parameters of the dominated path.In the second phase,based on the analogue beamforming matrices,the digital beamformers are designed to maximize the energy efficiency.Simulation results indicate that the proposed scheme can lead lower convergence time and complexity than the conventional schemes.In addition,the results show that the algorithm convergence time can be significantly reduced by increasing the positioning precision.