Research On Ultra Massive MIMO Near And Far Field Channel Estimation

With the rapid development of mobile communication technology,the communication frequency band is gradually moving from the 5 G millimeter wave frequency band to the 6G terahertz frequency band.As one of the key technologies of 6G,Extremely large-scale antenna array(ELAA)has become the cornerstone of the Ultra Massive MIMO(UMMIMO)system.As the size of the antenna array increases,the influence of the Rayleigh distance(Fresnel distance)on the channel estimation technology becomes non-negligible.The Rayleigh distance divides the near field and the far field in the communication system.In the near field,the electromagnetic waves reaching the antenna array are no longer parallel,which makes the traditional compressed sensing channel estimation technology no longer applicable.This thesis will take ELAA in the UMMIMO system as the main line.First,from the perspective of changing the channel environment,the RIS-assisted dual-structured sparse channel estimation scheme in the far field is studied.Furthermore,by increasing the size of the antenna array and using the Rayleigh distance to distinguish near and far fields,the channel estimation technology in the near-field scenario is studied.Due to the lack of research,RIS-assisted channel estimation has not been introduced in the near-field scenario.For RIS-assisted wireless communication systems,the dual-structured sparse properties of RIS cascaded channels are studied.Since the user channels with some common scatterers have similar statistical properties,the correlation gain brought by the joint sparsity between users can be further used to estimate the column common support of these users simultaneously and optimize column common support atom selection method.This thesis proposes a dual-structure portion simultaneous Orthogonal matching pursuit algorithm(Double Structured Portion Simultaneous Orthogonal Matching Pursuit,DS-PSOMP).The simulation results show that when the public support set accounts for half of the complete support set of the user column,and the number of concurrent service users is 16,the running time of the algorithm is reduced by 60%,and the channel estimation performance is still better than the literature algorithm.For the channel estimation problem in the near-field scenario,the polar domain will cause more serious energy leakage problems,and the orthogonality between the atoms of the measurement matrix is poor.Therefore,this thesis introduces the feature of weak selection to reduce the requirement of atomic orthogonality in the polar domain measurement matrix,and then uses the joint sparsity property of the channel model itself to design a simultaneous stagewise weak selection matching pursuit(Simultaneous Stagewise Weak OMP,SSWOMP)near-field polar domain Channel Estimation Scheme.Simulation results show that the performance of SSWOMP algorithm in polar domain is significantly better than that of Simultaneous Orthogonal Matching Pursuit(SOMP)algorithm and the original SWOMP algorithm.