Research on Beamspace Channel Estimation Method Based on DISTA

Abstract

Since the conventional Compressed Sensing (CS) algorithm in millimeter wave Massive Multi-Input Multi-Output (MIMO) system has the problem of low channel estimation accuracy, a deep learning based beamspace channel estimation method, Deep Iterative Shrinkage-Thresholding Algorithm (DISTA), is proposed. First, due to the sparsity of the beamspace channel, the beamspace channel estimation problem can be transformed into a sparse signal recovery problem; second, based on the iterative shrinkage threshold algorithm (ISTA), the channel state information (CSI) is sparsified using nonlinear transformation functions to replace the traditional manual transformation; finally, the iterative process of ISTA is expanded into a deep network, and the linear inverse transformation from the received pilot signal to the CSI is solved using the expanded network. The experimental results show that the proposed algorithm improves the NMSE performance gain by about 3 dB over the GM-LAMP algorithm when the signal-to-noise ratio (SNR) is 15 dB, and the algorithm accelerates the convergence speed compared with the conventional CS channel estimation algorithm.