Implicit vs. Explicit Approximate Matrix Inversion for Wideband Massive MU-MIMO Data Detection

Wu, Michael; Yin, Bei; Li, Kaipeng; Dick, Chris; Cavallaro, Joseph R.; Studer, Christoph

doi:10.1007/s11265-017-1313-z

Cited by 23 publications

(12 citation statements)

References 45 publications

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“…[58] [59]. However, the NS method recursion is slow, therefore, high-order recursion method such as Schulz recursion can be used to accelerate the NS recursion in expenses of extra computational complexity [60].…”

Section: A Linear Detectors Based On the Approximate Matrix Inversionmentioning

confidence: 99%

Massive MIMO Detection Techniques: A Survey

Albreem

Juntti

Shahabuddin

2019

IEEE Commun. Surv. Tutorials

366

229

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Section: A Linear Detectors Based On the Approximate Matrix Inversionmentioning

confidence: 99%

Massive MIMO Detection Techniques: A Survey

Albreem

Juntti

Shahabuddin

2019

IEEE Commun. Surv. Tutorials

366

229

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“…In spite of the significance of the direct algorithms-matrix decomposition, their analysis of complexity for massive MIMO systems and the differentiation with existing fixedpoint iteration-based algorithms and matrix inversion approximation algorithms is lacking in the literature [135], [137]. a) QR Decomposition Algorithm: The QR decomposition algorithm can be applied to get the solution of (10) as…”

Section: ) Linear Precoder Based On the Matrix Inversion Approximationmentioning

confidence: 99%

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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“…The results shown here are for a 128 × 8 (the notation represents B × U ) massive MU-OFDM-MIMO system with 1200 active subcarriers and a delay spread of L = 144. We assume an implicit Cholesky-based matrix inversion for data detection [35]. As demonstrated in [26], Gram matrix computation requires most of the complexity, as it scales quadratically in the number of BS antennas.…”

Section: A Complexity Comparisonmentioning

confidence: 99%

Approximate Gram-Matrix Interpolation for Wideband Massive MU-MIMO Systems

Jeon

Studer

2020

IEEE Trans. Veh. Technol.

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Linear and non-linear data-detection and precoding algorithms for wideband massive multi-user (MU) multipleinput multiple-output (MIMO) wireless systems that rely on orthogonal frequency-division multiplexing (OFDM) or singlecarrier frequency-division multiple access (SC-FDMA) often require computation of the Gram matrix for each active subcarrier. Computing the Gram matrix for each active subcarrier, however, results in excessively high computational complexity. In this paper, we propose novel, approximate algorithms that significantly reduce the complexity of Gram-matrix computation by simultaneously exploiting channel hardening and correlation across subcarriers. We show analytically that a small fraction of Gram-matrix computations in combination with approximate interpolation schemes are sufficient to achieve near-optimal errorrate performance at low computational complexity in massive MU-MIMO systems. We furthermore demonstrate that our approximate interpolation algorithms are more robust against channel-estimation errors than exact Gram-matrix interpolation algorithms that require high computational complexity.

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Implicit vs. Explicit Approximate Matrix Inversion for Wideband Massive MU-MIMO Data Detection

Cited by 23 publications

References 45 publications

Massive MIMO Detection Techniques: A Survey

Massive MIMO Detection Techniques: A Survey

Overview of Precoding Techniques for Massive MIMO

Approximate Gram-Matrix Interpolation for Wideband Massive MU-MIMO Systems

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