Off-Grid Compressed Channel Estimation with Parallel Interference Cancellation for Millimeter Wave Massive MIMO

Millimeter wave(mmWave)massive multiple-input multiple-output(MIMO)plays an im-portant role in the fifth-generation(5G)mobile com-munications and beyond wireless communication sys-tems owing to its potential of high capacity.How-ever,channel estimation has become very challenging due to the use of massive MIMO antenna array.For-tunately,the mmWave channel has strong sparsity in the spatial angle domain,and the compressed sensing technology can be used to convert the original channel matrix into the sparse matrix of discrete angle grid.Thus the high-dimensional channel matrix estimation is transformed into a sparse recovery problem with greatly reduced computational complexity.However,the path angle in the actual scene appears randomly and is unlikely to be completely located on the quanti-zation angle grid,thus leading to the problem of power leakage.Moreover,multiple paths with the random distribution of angles will bring about serious inter-path interference and further deteriorate the perfor-mance of channel estimation.To address these off-grid issues,we propose a parallel interference cancellation assisted multi-grid matching pursuit(PIC-MGMP)al-gorithm in this paper.The proposed algorithm con-sists of three stages,including coarse estimation,re-fined estimation,and inter-path cyclic iterative inter-ference cancellation.More specifically,the angular resolution can be improved by locally refining the grid to reduce power leakage,while the inter-path interfer-ence is eliminated by parallel interference cancellation(PIC),and the two together improve the estimation ac-curacy.Simulation results show that compared with the traditional orthogonal matching pursuit(OMP)al-gorithm,the normalized mean square error(NMSE)of the proposed algorithm decreases by over 14dB in the case of 2 paths.