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.
Highlights
In the past three decades, a rapid data traffic growth has been witnessed where the average wireless transmission rate has been doubled every 18 months
To achieve a high data rate, high spectral efficiency (100 bps/Hz), and high mobility, several technologies are considered as the backbone of the 5G networks, such as the massive multiple-input multiple-output (MIMO), millimeter wave [7,8], the internet of things (IoT) [9], ultra dense networks (UDNs), visible light communications (VLC), the optical wireless communications (OWC), and the device to device (D2D) communications [10]
We propose efficient initialization methods of the approximate message passing (AMP) algorithm for base station (BS) detectors using free matrix inversion methods, namely, the successive over relaxation (SOR), the Gauss–Seidel (GS), and the Jacobi (JA)
Summary
In the past three decades, a rapid data traffic growth has been witnessed where the average wireless transmission rate has been doubled every 18 months. In the UL, an efficient detector is required to estimate the transmitted signal where the main issue with utilizing a large number of antennas is the high detection complexity involved. In [21], the computational complexity of linear detection mechanisms based on the QR, Cholesky, and LDL decomposition algorithms for different massive MIMO configurations is presented. The utilization of the AMP algorithm is extended for applications in linear estimation for massive MIMO systems, precoding, and multi-user detection [22]. The second scheme of detection in massive MIMO includes free-matrix-inversion methods to estimate the received signal. In other words, such methods have inversion-less terms. The SOR, the GS, and the JA are utilized to effectively initialize the AMP-based detector
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