Channel Estimation For Large Intelligent Interface Communication Systems

Large intelligent surface is a new low-cost and low-power auxiliary communication technology.By adjusting the phase or amplitude of incident signals,the large intelligent surface can change the transmission environment of wireless signals and improve the transmission efficiency of wireless signals.However,channel estimation of large intelligent surface communication systems is a challenging problem.The main challenge is that large intelligent surface communication systems have two-level sparse channels.One possible solution is using compressive sensing theory,which is the main topic of this thesis.This thesis first introduces the large intelligent surface technology,suggests the differences between the large intelligent surface technology,backscattering technology and relay technology,and analyzes the feasibility of applying the compressed sensing theory to the sparse channel estimation of large intelligent surface.Then,the channel modeling method of large intelligent surface is presented,and the characteristics of the channel model are analyzed.The two-stage channel of large intelligent surface is used as the combined channel for estimation.Finally,this thesis explores the channel estimation solution of large intelligent surface:(1)using the traditional channel estimation method of pilot signals.This thesis proposes a channel estimation algorithm based on least square method,defines the pilot frequency structure,and obtains the channel estimation parameters by using orthogonal pilot frequency and least square method.(2)channel estimation method based on compressed sensing.A channel estimation method based on compressed sensing theory is proposed in this thesis.This method solves the shortcomings of the traditional channel estimation method,and two algorithms,convex optimization algorithm and greedy algorithm,are proposed in this thesis.The performance and efficiency of the two algorithms are verified by simulation,and the performance of channel estimation based on compressed sensing theory and the performance of traditional channel estimation based on pilot channel are compared and analyzed.By comparing the running time and mean square error of the algorithm,we concluded that the convex optimization algorithm has better running efficiency.By comparing the symbol error rate of the algorithm,we concluded that the two algorithms presented in this thesis have better performance than the traditional channel estimation algorithms.