Channel Estimation Method Based on FDD Large-Scale MIMO Bayesian Compressive Sensing

Abstract

The invention belongs to the technical field of wireless communication, and provides an FDD-based large-scale MIMO Bayesian compressed sensing channel estimation method for obtaining accurate channel state information. The present invention designs a Gaussian prior model of mode coupling to describe the common sparsity among different antennas, wherein the coefficients in the channel vector are divided into groups of equal length, and each group has a common hyperparameter, so that each The coefficients of the group have the same sparsity; then, through the expectation maximization step, Bayesian inference is performed based on an iterative method, in which the channel coefficient is used as a hidden variable, and the hyperparameter is used as an unknown parameter; finally, the obtained channel vector The posterior mean is used as an estimate of the channel. The simulation shows that the BCS method proposed by the present invention is superior to similar methods to a large extent, and can reach the performance boundary of the ideal least squares algorithm as the baseline.

Description

technical field [0001] The invention belongs to the technical field of wireless communication, and in particular relates to a method for estimating a compressed sensing channel based on a sparse Bayesian model. Background technique [0002] Due to the great spatial freedom brought by large-scale antennas, massive MIMO (Multiple-Input Multiple-Output, Multiple-Input Multiple-Output) systems can improve spectrum and energy efficiency by several orders of magnitude; therefore, it is widely considered It is one of the key technologies of the next generation wireless system. [0003] In order to give full play to the characteristics of massive MIMO, the base station needs to accurately obtain uplink and downlink CSI (Channel State Information). The downlink CSI is the same; therefore, only the terminal needs to transmit the uplink pilot, and the base station estimates the corresponding signal CSI; when the pilot sequence is properly designed, the base station can accurately esti...

AI Technical Summary

Problems solved by technology

In order to reduce the training and calculation burden, some methods based on compressed sensing have been proposed; one of the schemes is to feed back the compressed measurement value of downlink CSI from each user to the base station first, and then the base station adopts a method based on Orthogonal Matching Pursuit (OMP, Orthogonal Matching Pursuit ) algorithm to jointly estimate the channel matrix of multiple users; however, this algorithm only considers gently fading channels, so it can only be applied to narrowband systems

In order to deal with frequency selective channels in broadband systems, an Adaptive Structured Subspace Pursuit algorithm (ASSP, Adaptive Structured Subspace Pursuit) was proposed. Due to the common sparsity of different antennas, the ASSP algorithm can achieve such The performance boundary of the algorithm, and has a moderate training overhead; but the performance of the A SSP algorithm is determined by the threshold p th decision, which needs to be carefully adjusted according to the SNR; while the SNR is not an available prior, this greatly limits the practical application of the ASSP algorithm

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