Comparative Analysis of Data Detection Techniques for 5G Massive MIMO Systems

Abstract

Massive multiple-input multiple-output (MIMO) is a backbone technology in the fifth-generation (5G) and beyond 5G (B5G) networks. It enhances performance gain, energy efficiency, and spectral efficiency. Unfortunately, a massive number of antennas need sophisticated processing to detect the transmitted signal. Although a detector based on the maximum likelihood (ML) is optimal, it incurs a high computational complexity, and hence, it is not hardware-friendly. In addition, the conventional linear detectors, such as the minimum mean square error (MMSE), include a matrix inversion, which causes a high computational complexity. As an alternative solution, approximate message passing (AMP) algorithm is proposed for data detection in massive MIMO uplink (UL) detectors. Although the AMP algorithm is converging extremely fast, the convergence is not guaranteed. A good initialization influences the convergence rate and affects the performance substantially together and the complexity. In this paper, we exploit several free-matrix-inversion methods, namely, the successive over-relaxation (SOR), the Gauss–Seidel (GS), and the Jacobi (JA), to initialize the AMP-based massive MIMO UL detector. In other words, hybrid detectors are proposed based on AMP, JA, SOR, and GS with an efficient initialization. Numerical results show that proposed detectors achieve a significant performance enhancement and a large reduction in the computational complexity.

Implementation of the deep learning method for signal detection in massive-MIMO-NOMA systems

“…Signal identification in M-MIMO systems is difficult; nevertheless, for several reasons, the same pilots broadcast by many users may interfere with one another in M-MIMO systems, resulting in pilot contamination. Pilot contamination can reduce the accuracy of channel estimates, which can affect data detection performance [ 10 ]. To address this problem, strategies such as pilot decontamination and adaptive pilot allocation have been used.…”

Efficient Hybrid Iterative Method for Signal Detection in Massive MIMO Uplink System over AWGN Channel

“…To achieve high data rates, energy e ciency, and spectral e ciency, 5G used one of the enabling technologies known as massive multiple input multiple output (Massive MIMO) [1]. Massive MIMO is the most enticing technology for 5G and beyond wireless access [2]. Massive MIMO is an advancement of current MIMO systems used in wireless networks that groups together a large number of antennas at the base station and serves many users at the same time as shown in Figure 1.…”