Research On Aerial Intelligent Reflecting Surfance-Assisted Massive MIMO System Capacity Optimization

With the rapid development of the information society,the mobile communication industry is facing significant changes and challenges.The increasing scarcity of wireless spectrum resources has made improving system capacity an urgent issue.The key enabling technology for the fifth generation(5G)mobile communication system is massive multiple input multiple output(MIMO)technology,which can be combined with beamforming,millimeter wave and other technologies to greatly improve power efficiency and spectral efficiency.Intelligent reflecting surface(IRS)is considered the core technology of next-generation mobile communication,which can effectively solve the problem of obstacles between base stations and users in complex communication scenarios.It has significant advantages in enhancing network coverage,spectrum efficiency,energy efficiency,and deployment costs.Aerial intelligent reflecting surface(AIRS),which places IRS on aerial platforms such as UAVs and hot air balloon,combines the high mobility/rotation characteristics of aerial platforms and the high-quality link characteristics provided by intelligent reflecting surface,and can provide a broader signal coverage,which has attracted more and more attention and research.Based on this,this thesis analyzes and studies the capacity optimization issue in massive MIMO systems aided by AIRS.The main work is as follows:Firstly,this thesis investigates the joint optimization problem of active beamforming of base stations,passive beamforming of AIRS,and deployment location design for a multi-user massive MIMO system aided by AIRS.Under the condition of known statistical channel state information,(CSI),a scheme of system traversal and rate optimization based on block coordinate descent(BCD)is proposed.This scheme first establishes a complex optimization model for jointly optimizing base station beamforming,AIRS deployment location,and AIRS passive beamforming.Then,using the BCD descent algorithm,the non convex optimization problem is decoupled into three easily manageable sub problems.Finally,Lagrange multiplier method,relaxation variable method and RMSProp gradient descent algorithm are used to solve the subproblem respectively.The simulation results show that the proposed optimization scheme effectively improves the traversal and speed of the system,and has good convergence.Secondly,considering the mobility of users in real scenarios,it is necessary for communication systems to use beam tracking technology to adjust beamforming in real-time to align the beam with the moving user,thereby improving the direction of signal transmission and increasing the system capacity.Therefore,combining the rotation characteristics of AIRS,the beam tracking problem of AIRS aided massive MIMO systems in mobile environments was studied.Firstly,a system capacity optimization model was established to optimize active beamforming,AIRS passive beamforming,and rotation angle at the base station.Then,an unscented kalman filter with cascaded angles(UKFWCA)beam tracking algorithm based on cascaded angles is proposed.The simulation results indicate that the proposed UKF-WCA algorithm has high tracking accuracy.At the same time,AIRS rotation plays an important role in improving system capacity.