Design of near‐optimal local likelihood search‐based detection algorithm for coded large‐scale MU‐MIMO system

Abstract

Massive multiuser multiple input multiple output (MU‐MIMO) system is aimed to improve throughput and spectral efficiency through a large number of antennas incorporated at the transmitter and/or receiver. However, the MU‐MIMO system usually suffers from interantenna interference (IAI) and multiuser interference (MUI). The IAI imposes due to closely spaced antennas at each user equipment (UE), and MUI is enforced when one user comes under the vicinity of another user in the same cellular network. Most of the previous literatures considered any one of these interferences. However, the present work proposes singular value decomposition (SVD) precoding‐assisted user‐level local likelihood ascent search (LLAS) algorithm to mitigate both IAI and MUI. In the uplink MU‐MIMO, the IAI is cancelled by SVD, and the residual MUI is mitigated by LLAS detection. The LLAS detection balances the trade‐off between the classical suboptimal likelihood ascent search (LAS) and optimal maximum likelihood (ML) detection techniques. The proposed LLAS performs local search among all 2MT‐dimensional neighborhood vectors at each UE, where MT represents number of transmitting antennas of each UE. Thus, its performance is near optimal, and its complexity is much lower than ML detector.

Deep graph neural network optimized with fertile field algorithm based detection model for uplink multiuser massive multiple‐input and multiple‐output system

“…Improving the lower complex detection approach for uplink higher order adjusted massive multiple input multiple output systems is a significant problems 5,6 . Nowadays, massive multiple input multiple output schemes include binary phase shift keying and 4‐quadrature amplitude modulation signals for low‐order modulation signals, several detection approaches determined the approximate optimal performance of bit error rate utilizing the maximal likelihood algorithm along polynomial computational complexity, contains the likelihood ascents search (LAS), mixed Gibbs sampling (MGS), and ant colony optimization detection algorithm 7‐9 . These models have inefficient for detecting high‐order modulation signals owing to worst performance of bit error rate, 16‐quadrature amplitude modulation, 64‐quadrature amplitude modulation signals 10‐14 .…”

“…5,6 Nowadays, massive multiple input multiple output schemes include binary phase shift keying and 4-quadrature amplitude modulation signals for low-order modulation signals, several detection approaches determined the approximate optimal performance of bit error rate utilizing the maximal likelihood algorithm along polynomial computational complexity, contains the likelihood ascents search (LAS), mixed Gibbs sampling (MGS), and ant colony optimization detection algorithm. [7][8][9] These models have inefficient for detecting high-order modulation signals owing to worst performance of bit error rate, 16-quadrature amplitude modulation, 64-quadrature amplitude modulation signals. [10][11][12][13][14] In lesser or medium scale MIMO systems, semi definite relaxation decoder, LLLSDR detection, MGS-MR detection, hybrid RTS-BP detection gives results of high order modulation signals.…”

Equivalent MIMO Channel Matrix Sparsification for Enhancement of Sensor Capabilities

“…There are two directions for improving MIMO detectors: simplifying the ML detector with losses in the energy efficiency and complicating the MMSE detector by increasing the energy efficiency. The first direction includes algorithms based on spherical decoding, e.g., K-best [6,8,9]. The second direction includes various iterative algorithms using the MMSE detector as a basis, e.g., ESA, EPA, and V-BLAST detector [2,[10][11][12][13][14][15][16].…”

Low‐complexity sphere decoding for MIMO‐SCMA systems

“…On the other hand, multiple-input multiple-output (MIMO) has also been viewed as the key technique that is able to improve spectral efficiency [3][4][5][6][7] and has been applied in several wireless communication systems for a long time [8]. To date, researchers have investigated the combination of MIMO and SCMA, namely MIMO-SCMA, to further improve the spectral efficiency and support massive connectivity [9][10][11][12].…”