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

Abstract

Summary 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 Ofalse(NT2NRfalse) to O(ωNTNR) where ω < <2NT.

Efficient detectors for uplink massive MIMO systems

“…In [29], the banded matrix accelerates the GS, JA, and SOR's convergence rate. The banded matrix is exploited in [30] to reduce the computational complexity of the likelihood ascent search (LAS) based detector. One drawback of the above methods is their performance deterioration in an imperfect CSI environment.…”

“…As the equalization matrix in MMSE is diagonally dominant, the diagonal matrix has conventionally played a key role in formulating most approximate inversion methods and iterative methods for massive MIMO detection. Several recent works, however, have established the merit of other formulations, such as the band matrix [30] and the stair matrix [28]. Both types of matrices will play essential roles in the efficient initialization techniques proposed in our work.…”

Performance analysis and comparison of approximate detectors with linear detectors in massive MIMO systems under imperfect channel state information

“…Most of the work carried out considers perfect CSI (PCSI) at the receiver for the evaluation of SINR, symbol error rate (SER), and BER performances. The performance of ZF and MMSE under PCSI is analyzed in References 29,30, and the approximate detectors NS and GS are described in References 31,32. Practically, it is hard to attain PCSI because of feedback delays and imperfections in information extraction that result in imperfect CSI (ICSI).…”

Soft-Output Deep-LAS Detection for Coded MIMO System: A Learning-Aided LLR Approximation

“…proposed a random list-based LAS algorithm for a large-scale uplink multiuser MIMO system, and they used the LAS algorithm to improve the performance of hybrid detectors in MIMO system with imperfect channel state information (CSI) in [27]. Similarly, the unconstrained neighborhood search based LAS algorithm was analyzed in [28].…”