Research On Joint Beamforming And UAV Clustering Optimization Method For Cellular Communication Assisted By UAV Equipped With Intelligent Reflecting Surfaces

With the rapid development of Internet of things(IOT)and artificial intelligence,the future mobile communication network should meet the requirements of high data transmission rate,low delay and massive connection,and can establish an "air-space-earth" integrated communication network with the help of unmanned aerial vehicles(UAV),satellites and other communication equipment to realize global wireless connection services.Among them,by taking advantage of the high mobility and flexible deployment of UAV,UAV Communication can realize the transmission mode dominated by line of sight(LOS)path,so as to effectively improve the communication quality and expand the communication coverage.Traditional UAV communication usually requires the installation of signal transceiver equipment on UAV.UAV can complete the signal forwarding as air relay or base station(BS).Because the signal transceiver equipment is generally active and has large energy loss,the working time of UAV in the air is limited and the communication efficiency of the system is reduced.In 6G mobile communication,a key technology is intelligent reflecting surface(IRS),which can intelligently reflect the signal by using a large number of low-cost passive reflective elements,effectively improve the signal transmission quality,and has significant advantages in improving data rate and energy efficiency.However,in the current research,the intelligent reflecting surface is usually deployed on the building surface and can not move flexibly,which leads to the problem of signal blocking and can not give full play to its advantage of dynamically configuring the radio environment.Therefore,carrying intelligent reflecting surface on UAV for communication can not only realize flexible deployment and expand coverage,but also effectively reduce energy consumption and cost and improve communication quality.It is a new technology with development potential.Based on this,aiming at the cellular communication system assisted by UAV equipped with intelligent reflecting surface,this paper studies the beamforming between base station and intelligent reflecting surface and UAV clustering.The main innovations are as follows:(1)Based on the block sparse optimization theory,the joint optimization model of base station and intelligent reflecting surface beamforming and UAV clustering is established.Firstly,the joint beamforming and UAV selection matrix is constructed,which is regularized by L1 / L2 norm and introduced into the objective function as a penalty function.Therefore,the beamforming optimization of base station and intelligent reflecting surface and UAV clustering are modeled as a group lasso block sparse optimization model to realize the joint optimization of beamforming and clustering,so as to improve the system sum rate and resource utilization.(2)Since the established optimization problem is NP hard problem,in order to solve it with low complexity,a two-stage optimization method of joint base station and intelligent reflecting surface beamforming and UAV clustering is proposed: firstly,aiming at the intelligent reflecting surface beamforming sub problem,the reflection angle of the intelligent reflecting surface is iteratively optimized by using the maximization minimization(mm)optimization theory.Furthermore,aiming at the sub problems of base station beamforming and UAV clustering,a joint base station beamforming and UAV clustering algorithm based on block sparsity optimization is proposed.By block sparsity of the joint beamforming and UAV selection matrix,the UAV clusters serving different users and the transmit beamforming matrix at the base station are obtained while maximizing the system sum rate.Finally,through detailed experimental simulation,the beamforming of joint base station and intelligent reflecting surface and UAV clustering optimization algorithms proposed in this paper are verified and compared with the traditional methods.The experimental results show that the proposed algorithm can greatly improve the communication system and speed according to the actual cell situation,and effectively cluster the UAV.