Research On Transmission Technology For Reconfigurable Intelligent Surface Assisted Multi-User Wireless Communication Systems

Reconfigurable intelligent surface(RIS)is composed of a large number of lowpower and low-cost passive reflecting elements,which builds an intelligent and reconfigurable wireless communication environment by digitially controlling the signal reflection in real time,so as to realize multiple purposes such as capacity improvement,coverage enhancement,interference suppression.However,the integration of RIS and wireless communication systems still needs to address multiple challenges,especially the efficient RIS channel estimation method and RIS beamforming design,so as to fully exploit RIS's performance potential.Therefore,this thesis focuses on the transmission technology for RIS assisted multi-user wireless communication systems,and specifically investigates the channel estimation method for RIS assisted wireless communication systems and the beamforming design for RIS assisted multicast symbiotic communication system.On the one hand,the thesis investigates the channel estimation method for RIS assisted wireless communication systems to provide accurate channel state information for the subsequent RIS beamforming design.Firstly,for the single-user scenario,a singleuser channel estimation method based on the structured sparsity is proposed by analyzing and utilizing the row-block and column-block sparse structure and two-timescale characteristics of single-user channel.Then,for the multi-user scenario,further utilizing the common row-block and column-block sparse structure characteristics of multi-user channel on the basis of the characteristics of single-user channel,a multi-user joint channel estimation method based on the structured sparse Bayesian learning is proposed.Simulation results show that when the normalized mean square error is 0.01,the pilot overhead of the proposed channel estimation methods is reduced by 58% compared with the traditional orthogonal matching pursuit algorithm.In addition,they are insensitive to channel sparsity,correlation parameters,and the number of common paths,and has the advantages of strong robustness and low complexity,which are suitable for the practical scenario where the specific structural information of the channel is unknown and changing.On the other hand,the thesis investigates an RIS assisted multicast symbiotic communication system,where the base station transmits multicasting signals to multiple primary receivers with the assistance of RIS,and the RIS modulates the information of the Internet-of-Things into multicasting signals to transmit to the receiver of the Internetof-Things,so as to realize the mutualism of the multicast transmission and the Internetof-Things transmission.Under the practical constraints,the active beamforming of the base station and the passive beamforming of the RIS are jointly optimized to minimize the transmit power of the base station.For the sake of an efficitive solution of the nonconvex power minimization problem,two optimization subproblems are formed by decoupling the optimization variables according to the alternating optimization strategy,and the quadratic transform and semidefinite relaxation algorithms are adopted to solve the subproblems comprehensively,and then an iterative optimization algorithm for jointly active and passive beamforming design is proposed.Simulation results show that when the signal-to-interference-plus-noise ratio requirement of the primary receivers is greater than 10 d B,the required power of the proposed RIS assisted multicast symbiotic communication system is reduced by more than 60% compared with the traditional multicast communication system without RIS,and compared with the RIS purely assisted multicast communication system without information transmission,the proposed system can enhance the performance of the multicast transmission and realize the Internet-ofThings transmission at the same time,and the additional power consumption is less than 3%.