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 important role in the fifth-generation (5G) mobile communications and beyond wireless communication systems owing to its potential of high capacity. However, channel estimation has become very challenging due to the use of massive MIMO antenna array. Fortunately, 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 quantization angle grid, thus leading to the problem of power leakage. Moreover, multiple paths with the random distribution of angles will bring about serious interpath interference and further deteriorate the performance of channel estimation. To address these off-grid issues, we propose a parallel interference cancellation assisted multi-grid matching pursuit (PIC-MGMP) algorithm in this paper. The proposed algorithm consists of three stages, including coarse estimation, refined estimation, and inter-path cyclic iterative interference cancellation. More specifically, the angular resolution can be improved by locally refining the grid to reduce power leakage, while the inter-path interference is eliminated by parallel interference cancellation (PIC), and the two together improve the estimation accuracy. Simulation results show that compared with the traditional orthogonal matching pursuit (OMP) algorithm, the normalized mean square error (NMSE) of the proposed algorithm decreases by over 14dB in the case of 2 paths.