Likelihood ascent search-aided low complexity improved performance massive MIMO detection in perfect and imperfect channel state information

Massive multiple-input multiple-output (MIMO) systems improve spectral efficiency and link reliability. Linear minimum mean-squared error (MMSE) detectors can achieve optimal performance in massive MIMO detection but require large dimension matrix inversion, which is computationally intensive. Therefore, low complexity iterative detection schemes are proposed in the literature as an alternative to the exact MMSE method. However, the performance of these schemes is greatly influenced by the choice of the initial solution. Therefore, to improve the detection performance in this paper, we proposed three hybrid detection schemes, which are Newton-Schultz-Richardson (NS-RI), Newton-Schultz-Chebyshev (NS-Cheby), and Newton-Schultz-Gauss-Seidel (NS-GS). The proposed hybrid schemes show significant performance improvement and a higher convergence rate compared to their original counterpart. The performance of the proposed detectors is further improved by the likelihood ascent search (LAS) stage, which corrects the detected symbols obtained from iterative MMSE methods through a neighborhood search. However, the complexity of the LAS algorithm primarily depends on the initialization step. In this work, we introduce an efficient Gram matrix computation in the real domain. Additionally, we have applied a band approximation of the Gram matrix for the LAS initialization, which reduces the order of computational complexity of the Gram matrix from O(NT2NR) to O(omega NTNR) where omega < N-T.