Reduced complexity K-best sphere decoding algorithms for ill-conditioned MIMO channels

Al-Nahhal, Ibrahim; Alghoniemy, Masoud; Muta, Osamu; El-Rahman, Adel B. Abd

doi:10.1109/ccnc.2016.7444753

Cited by 6 publications

(6 citation statements)

References 11 publications

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“…Besides the cost of a matrix inversion, a challenge in matrix inversion lies on when the channel matrix is nearly singular and the system becomes ill-conditioned. In this case, the matrix inversion will not equalize the received signal [55], [56]. In order to overcome the inherent noise enhancement, modified detectors with approximate matrix inversion methods will be an essential.…”

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

339

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

339

229

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“…Unfortunately, these detectors suffer from a high performance loss and high computational complexity when the massive MIMO size is large or the ratio between the BS antennas and user antennas is close to 1. Other detectors require a decomposition which increases the computational complexity [113], [114]. Therefore, most of proposed detectors are not feasible in implementation due to a high computational complexity.…”

Section: Overview Of Detection Schemes In Massive Mimomentioning

confidence: 99%

Deep Learning for Massive MIMO Uplink Detectors

Albreem

Alhabbash²,

Shahabuddin

et al. 2022

IEEE Commun. Surv. Tutorials

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Detection techniques for massive multiple-input multiple-output (MIMO) have gained a lot of attention in both academia and industry. Detection techniques have a significant impact on the massive MIMO receivers' performance and complexity. Although a plethora of research is conducted using the classical detection theory and techniques, the performance is deteriorated when the ratio between the numbers of antennas and users is relatively small. In addition, most of classical detection techniques are suffering from severe performance loss and/or high computational complexity in real channel scenarios. Therefore, there is a significant room for fundamental research contributions in data detection based on the deep learning (DL) approach. DL architectures can be exploited to provide optimal performance with similar complexity of conventional detection techniques. This paper aims to provide insights on DL based detectors to a generalist of wireless communications. We garner the DL based massive MIMO detectors and classify them so that a reader can find the differences between various architectures with a wider range of potential solutions and variations. In this paper, we discuss the performance-complexity profile, pros and cons, and implementation stiffness of each DL based detector's architecture. Detection in cell-free massive MIMO is also presented. Challenges and our perspectives for future research directions are also discussed. This article is not meant to be a survey of a mature-subject, but rather serve as a catalyst to encourage more DL research in massive MIMO.

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“…There is a plethora of research to approximate or avoid the matrix inversion of G rather than computing it [111]. In addition to the high complexity of a matrix inversion, a defy in matrix inversion is the inversion of nearly singular and ill-conditioned matrix [112]. To beat the inveterate noise boost, advanced precoders with approximate/avoid matrix inversion methods are required.…”

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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Reduced complexity K-best sphere decoding algorithms for ill-conditioned MIMO channels

Cited by 6 publications

References 11 publications

Massive MIMO Detection Techniques: A Survey

Massive MIMO Detection Techniques: A Survey

Deep Learning for Massive MIMO Uplink Detectors

Overview of Precoding Techniques for Massive MIMO

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