Approximative Matrix Inversion Based Linear Precoding for Massive MIMO Systems

Qiang, Xiaowei; Liu, Yang; Feng, Qingxia; Liu, Jinhong; Ren, Xueli; Jin, Minglu

doi:10.1109/icnc47757.2020.9049670

Cited by 7 publications

(11 citation statements)

References 17 publications

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“…• It has a near-optimal performance of the ZF algorithm when the ratio of (M/N ) is large [16], [100]. • It has a marginal lower complexity than the ZF algorithm when the ratio of (M/N ) is large [100].…”

Section: Nsamentioning

confidence: 98%

“…The basic linear precoder mainly depends on multiplying the transmitted signal a with the precoding matrix P. The basic linear precoder has O(N 3 ) computational complexity which is comparable to the exact matrix inversion complexity [16], [99], [100]. a) Maximum Ratio Transmission (MRT) Algorithm: The MRT aims to maximize the gain of signal into a specific receive terminal.…”

Section: Massive Mimo Precoding Techniquesmentioning

confidence: 99%

“…The NSA algorithm has more energy efficiency and low complexity than the other traditional inversion algorithms i.e. ZF, and MMSE algorithms [100], [116], [117]. This is because the fact that the G matrix becomes diagonally dominant as the value (M/N ) ratio increases [107].…”

Section: ) Linear Precoder Based On the Matrix Inversion Approximationmentioning

confidence: 99%

“…In [100], a precoding technique using the joint CI and NSA (CI-NSA) algorithm is proposed to achieve the near-optimal performance. The CI-NSA algorithm optimizes the NSA algorithm by CI method, which converges faster than the other existing NSA precoding algorithms.…”

Section: ) Linear Precoder Based On the Matrix Inversion Approximationmentioning

confidence: 99%

“…• It offers a near-optimal performance of the ZF algorithm under correlated and uncorrelated channels [100], [131]. • It offers a good direction for searching process for the NSA algorithm [100], [131].…”

Section: Ci-nsamentioning

confidence: 99%

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Overview of Precoding Techniques for Massive MIMO

et al. 2021

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Massive multiple-input multiple-output (MIMO) is playing a crucial role in the fifth generation (5G) and beyond 5G (B5G) communication systems. Unfortunately, the complexity of massive MIMO systems is tremendously increased when a large number of antennas and radio frequency chains (RF) are utilized. Therefore, a plethora of research efforts has been conducted to find the optimal precoding algorithm with lowest complexity. The main aim of this paper is to provide insights on such precoding algorithms to a generalist of wireless communications. The added value of this paper is that the classification of massive MIMO precoding algorithms is provided with easily distinguishable classes of precoding solutions. This paper covers linear precoding algorithms starting with precoders based on approximate matrix inversion methods such as the truncated polynomial expansion (TPE), the Neumann series approximation (NSA), the Newton iteration (NI), and the Chebyshev iteration (CI) algorithms. The paper also presents the fixed-point iteration-based linear precoding algorithms such as the Gauss-Seidel (GS) algorithm, the successive over relaxation (SOR) algorithm, the conjugate gradient (CG) algorithm, and the Jacobi iteration (JI) algorithm. In addition, the paper reviews the direct matrix decomposition based linear precoding algorithms such as the QR decomposition and Cholesky decomposition (CD). The non-linear precoders are also presented which include the dirty-paper coding (DPC), Tomlinson-Harashima (TH), vector perturbation (VP), and lattice reduction aided (LR) algorithms. Due to the necessity to deal with a high consuming power by the base station (BS) with a large number of antennas in massive MIMO systems, a special subsection is included to describe the characteristics of the peak-to-average power ratio precoding (PAPR) algorithms such as the constant envelope (CE) algorithm, approximate message passing (AMP), and quantized precoding (QP) algorithms. This paper also reviews the machine learning role in precoding techniques. Although many precoding techniques are essentially proposed for a small-scale MIMO, they have been exploited in massive MIMO networks. Therefore, this paper presents the application of small-scale MIMO precoding techniques for massive MIMO. This paper demonstrates the precoding schemes in promising multiple antenna technologies such as the cell-free massive MIMO (CF-M-MIMO), beamspace massive MIMO, and intelligent reflecting surfaces (IRSs). In-depth discussion on the pros and cons, performance-complexity profile, and implementation solidity is provided. This paper also provides a discussion on the channel estimation and energy efficiency. This paper also presents potential future directions in massive MIMO precoding algorithms.

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Section: Nsamentioning

confidence: 98%

Section: Massive Mimo Precoding Techniquesmentioning

confidence: 99%

Section: ) Linear Precoder Based On the Matrix Inversion Approximationmentioning

confidence: 99%

Section: ) Linear Precoder Based On the Matrix Inversion Approximationmentioning

confidence: 99%

Section: Ci-nsamentioning

confidence: 99%

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Overview of Precoding Techniques for Massive MIMO

et al. 2021

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

Khurshid

Imran

Wakeel

2023

Trans Emerging Tel Tech

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SummaryMassive MIMO systems are a key technology for enhancing the capacity and reliability of wireless communication systems. However, to unlock the full potential of massive MIMO systems, accurate channel state information is crucial at the receivers. In practice, channel estimation errors are inevitable, leading to significant performance degradation in traditional massive MIMO equalizers that assume perfect channel state information. In this work, we investigate the performance of approximate massive MIMO detectors, specifically the Gauss‐Seidel and Neumann series, over a time‐invariant channel with imperfect channel state information. We derive the expressions of signal‐to‐interference‐plus‐noise ratio and average bit error rate for approximate massive MIMO detectors in a unicellular environment using M‐ary quadrature amplitude modulation. Additionally, we compare the performance of linear detectors, including zero‐forcing, minimum‐mean‐squared‐error, and maximal‐ratio combining, with that of the approximate detectors. Through extensive Monte Carlo numerical simulations and various system configurations, we demonstrate that the Gauss‐Seidel detector, under imperfect channel state information, achieves comparable bit error rate performance to the zero‐forcing and minimum‐mean‐squared‐error detectors while requiring less computational complexity. Our study suggests that the Gauss‐Seidel detector has the potential to be used in massive MIMO systems in the presence of imperfect channel state information, highlighting its practical relevance in real‐world scenarios.

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