Beamforming and Precoding Techniques

Darsena, Donatella; Gelli, Giacinto; Verde, Francesco

doi:10.1002/9781119471509.w5gref020

Cited by 9 publications

(7 citation statements)

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“…As a result, the layout of an effective beamforming method is obtained through optimal channel estimation. The primary categories of beamforming architectures are analog, digital, and hybrid beamforming architectures (Figure 3) [39], [40]. In traditional multi-user MIMO systems, fully-digital precoding is the typical approach to adjust the amplitudes and phases of the transmitted signals.…”

Section: Figure 1 Frequency Rangesmentioning

confidence: 99%

“…To reduce IUI, several precoding approaches have been applied. There are primarily three categories of beamforming architectures that are commonly explored [39], [40]: analog, digital, and hybrid beamforming architecture as shown in Figure 3.…”

Section: B Linear Precoding Techniquesmentioning

confidence: 99%

“…It also offers a high beam gain for wireless systems, allowing them to overcome the severe path loss challenges posed by mmWave as well as hardware limitations in massive MIMO schemes [87]. Basically, the three categories of beamforming architectures are analog, digital, and hybrid beamforming architectures [39], [40].…”

Section: A Millimeterwave-massive Mimo Beamformingmentioning

confidence: 99%

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Precoding and Beamforming Techniques in mmWave-Massive MIMO: Performance Assessment

et al. 2022

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Massive MIMO and mmWave communication are the technologies for achieving 5G design goals. Fortunately, these two technologies share a symbiotic integration. As a result, amalgamating mmWave communications with massive MIMO forms, "mmWave-massive MIMO," which significantly improves spectral and energy efficiency. It also achieves high multiplexing gains and increases mobile network capacity. However, massive MIMO, mmWave communications, and mmWave-massive MIMO systems have been studied independently. Consequently, this article explores the ideas, performances, comparisons, and discussions of these three 5G technologies jointly, considering their precoding and beamforming methods. On the other hand, the complexity of these technologies increases when a large number of antennas and radio frequencies (RFs) are used. Thus, several investigations are going on to search for the appropriate precoding and beamforming strategies with low cost, power, and complexity. Therefore, massive MIMO linear precoding techniques such as zero-forcing, maximum ratio transmission, regularized zero-forcing, truncated polynomial expansion and phased zero forcing are addressed in this work. In addition, the most common non-linear precoding schemes: dirty-paper coding, Tomlinson-Harashima, and vector perturbation, are presented. Furthermore, a detailed discussion of the beamforming techniques called analog, digital, and hybrid analog-digital beamforming schemes is included. We also examine the potential of hybrid analog-digital beamforming with its fully-connected and sub-connected architecture approaches in making mmWave massive MIMO a reality. We evaluate their performance with the parameters: bit error rate, signal to noise ratio, complexity, spectral efficiency, and energy efficiency. According to the analytical and simulation results, the partially-connected hybrid analog/digital beamforming architecture offers better allover performance for mmWave-Massive MIMO communications by compromising: power consumption, cost, complexity, and performance. Finally, the potential future directions in mmWave-massive MIMO precoding and beamforming challenges are addressed.

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Section: Figure 1 Frequency Rangesmentioning

confidence: 99%

Section: B Linear Precoding Techniquesmentioning

confidence: 99%

Section: A Millimeterwave-massive Mimo Beamformingmentioning

confidence: 99%

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Precoding and Beamforming Techniques in mmWave-Massive MIMO: Performance Assessment

et al. 2022

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“…• It has a promising performance, by employing SIC of the data streams at the transmitter, where it overcomes other linear precoding algorithms performance [208], [209]. • It has a practical implementation in contrast with the DPC algorithm [98].…”

Section: Thmentioning

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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“…Beamforming restricts transmitted signals to a particular desired receiving antenna element available in an antenna array at the receiver’s end. Consequently, for achieving the high array, diversity and multiplexing gain, beamforming plays an important role in mmWave MIMO systems [ 4 ]. Generally, three kind of beamforming techniques—analog beamforming (ABF), digital beamforming (DBF) and hybrid beamforming (HBF)—are used.…”

Section: Introductionmentioning

confidence: 99%

On the Performance of Efficient Channel Estimation Strategies for Hybrid Millimeter Wave MIMO System

Srivastav

Chen

Wahla

2020

Entropy

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Millimeter wave (mmWave) relying upon the multiple output multiple input (MIMO) is a new potential candidate for fulfilling the huge emerging bandwidth requirements. Due to the short wavelength and the complicated hardware architecture of mmWave MIMO systems, the conventional estimation strategies based on the individual exploitation of sparsity or low rank properties are no longer efficient and hence more modern and advance estimation strategies are required to recapture the targeted channel matrix. Therefore, in this paper, we proposed a novel channel estimation strategy based on the symmetrical version of alternating direction methods of multipliers (S-ADMM), which exploits the sparsity and low rank property of channel altogether in a symmetrical manner. In S-ADMM, at each iteration, the Lagrange multipliers are updated twice which results symmetrical handling of all of the available variables in optimization problem. To validate the proposed algorithm, numerous computer simulations have been carried out which straightforwardly depicts that the S-ADMM performed well in terms of convergence as compared to other benchmark algorithms and also able to provide global optimal solutions for the strictly convex mmWave joint channel estimation optimization problem.

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Beamforming and Precoding Techniques

Cited by 9 publications

References 40 publications

Precoding and Beamforming Techniques in mmWave-Massive MIMO: Performance Assessment

Precoding and Beamforming Techniques in mmWave-Massive MIMO: Performance Assessment

Overview of Precoding Techniques for Massive MIMO

On the Performance of Efficient Channel Estimation Strategies for Hybrid Millimeter Wave MIMO System

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