Research On Beamforming Algorithm For NOMA System Based On IRS

Non-orthogonal multiple access(NOMA)is a key technology for next-generation wireless communication systems to improve spectrum utilization,balance user fairness,and expand network connections.Intelligent reflecting surface(IRS)technology can control channel conditions by adjusting the phase shift and amplitude of IRS components.Therefore,IRS has significant potential in improving the coverage,throughput,and energy efficiency of communication systems.In addition,IRS can provide NOMA with ideal differential channels,which will facilitate the realization of NOMA.IRS can also enhance beamforming to further reduce inter-user interference.Therefore,this thesis will use the convex optimization method to design the beamforming of the NOMA system based on the intelligent reflector,aiming to further improve the energy utilization and spectrum efficiency of the system,and expect to achieve a compromise between complexity and performance.To solve the problems of large inter cluster interference and poor channel environment of cell edge users in non-orthogonal multiple access networks,a joint algorithm based on semidefinite relaxation(SDR)is proposed to design base station beamforming and reflector phase shift matrix.Considering the non-convex constraint of SINR,the quadratic constraint is transformed into affine constraint by increasing the dimension of optimization variables.Finally,the two subproblems of transmit power minimization and phase shift feasibility are solved iteratively by using the alternating optimization method.In order to solve the problem of performance degradation of SDR algorithm in solving large-scale problems,a low complexity algorithm based on successive convex approximation(SCA)is proposed.The original problem is solved iteratively by re-laxing the constant modulus constraint of reflector.Simulation results show that SCA algorithm can approach the perfor-mance of SDR algorithm with low complexity,and the performance of both algorithms is better than the existing algorithms.This thesis further extends the beamforming design of the Multiple-Input Single-Output NOMA system to the Multiple-Input Multiple-Output NOMA system,and proposes a block coordinate descent algorithm based on Quadratic Transform(QT)to iteratively optimize the beamforming matrices at the base station,IRS,and users.In order to solve the problem quickly,a distributed algorithm based on the Alternating Direction Method of Multipliers(ADMM)framework is further proposed.The problem is decomposed into multiple independent sub-problems through the idea of decomposition and coordination,and the closed solution of each sub-problem is obtained.The simulation results show that the proposed algorithm can achieve the sum rate performance close to the interior point method with lower complexity,and is significantly better than other methods.