Taxonomy and Performance Evaluation of Hybrid Beamforming for 5G and Beyond Systems

Rihan, Mohamed; Soliman, Tarek Abed; Xu, Chen; Dessouky, Moawad I.

doi:10.1109/access.2020.2984548

Cited by 34 publications

(24 citation statements)

References 133 publications

(213 reference statements)

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“…In [65], a hybrid precoding scheme based on equal gain transmission and the ZF has been proposed to reduce the hardware cost and processing complexity of massive MIMO systems. Detailed explanations of hybrid precoding with hardware architectures and methods of deployment with the impact of CSI have been comprehensively discussed in [66]. It was shown that the hybrid beamforming has a critical impact in minimizing the hardware cost since a small number RF chains at the transceivers was utilized.…”

Section: Signal Processing Challengesmentioning

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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Section: Signal Processing Challengesmentioning

confidence: 99%

Overview of Precoding Techniques for Massive MIMO

et al. 2021

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“…In this article, our contributions are as follows: Recent literature studies [3], [36], [37] have discussed multi-beam antennas based on beamforming circuits, optical components, and different phase switching methods, in general. Although, they failed to discuss in detail the different types of beamforming circuits based on the BM number of layers.…”

Section: B Contributions and Organization Of This Articlementioning

confidence: 99%

Butler Matrix Based Beamforming Networks for Phased Array Antenna Systems: A Comprehensive Review and Future Directions for 5G Applications

et al. 2021

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Due to the rapid development of wireless communication technologies, the number of wireless users are radically increasing. Currently, ∼23 billion wireless devices are connected to the internet, and these numbers are expected to increase manifolds in the years to come. The technology growth of the fifth-generation (5G) wireless systems will be needed to meet this high demand of the network. 5G wireless systems offer data-rates of up to 10Gbps, 1-ms latency, and reduced power consumption. It is a known fact that 5G wireless systems will be exploiting beyond the presently used 3 GHz microwave and millimetre-wave (mm-wave) frequency bands. This is the primary driver in the development of the 5G wireless system. Multi-beam Phased array antenna (PAA) systems are typically used in the deployment of 5G systems for high-gain and directionality. In current 5G and future Beyond 5G (B5G) antenna array systems, beamforming networks (BFNs) such as the Butler Matrix (BM) will play a key role in achieving multi-beam characteristics. So, this paper presents an extensive review of the BM based BFNs, and discusses which type of BM will be suitable for the phased array antenna (PAA) systems in the upcoming 5G and next-generation of B5G wireless systems. Moreover, this paper also summarizes the different types of BM designs based on the number of layers. The BMs are classified into the bi-layer, tri-layer, and four-layer structures. It includes different techniques that have been used to solve the problem of crossing, narrow bandwidth, and size reduction of the BM. From the previous studies, it is found that most of the past research work was performed using the bi-layer BM system, whereas the difficult geometries like tri-and four-layer BM are avoided due to their complex fabrication process. It is also found in this paper that the metamaterial (MTM) based bi-layer BM achieves low insertion-loss and phase-error, excellent bandwidth and compact size, and good S-parameter performance, which makes them an ideal BFN candidate for the upcoming 5G and next-generation B5G systems.INDEX TERMS Butler matrix (BM), metamaterial (MTM), 5G, beyond 5G (B5G), beamforming network (BFN), phased array antenna (PAA) systems.

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“…The RSS of the l th path is P l = P t +G t +G r −L [36], where P t is the transmit power, G t is the transmit antenna gain, G r is the receive antenna gain, L is the path loss obtained by (5) for the LoS path and by ( 5) and ( 7) for the single-reflection path. For the non-existent path, P l = −∞.…”

Section: ) Path Representationmentioning

confidence: 99%

“…Massive multi-input multi-output (mMIMO) systems with an extremely large number of antenna elements can significantly improve the spectral and energy efficiency in millimeter-wave (mmWave) frequencies via focusing narrow and high-gain beams towards a small region and thus increase the overall data rate and channel capacity via spatial multiplexing [2]. For downlink transmission, multi-user mMIMO (MU-mMIMO) using hybrid precoding (HP) is prevalently deployed at the BS to tackle above-mentioned challenges, where HP supports a large number of antenna elements via a concatenation of a radio frequency (RF) beamformer, few power-hungry RF chains and a low-dimension baseband precoder [3]- [5].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning and Geospatial Data-Based Channel Estimation Technique for Hybrid Massive MIMO Systems

et al. 2021

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This paper presents a novel channel estimation technique for the multi-user massive multiple-input multiple-output (MU-mMIMO) systems using angular-based hybrid precoding (AB-HP). The proposed channel estimation technique generates group-wise channel state information (CSI) of user terminal (UT) zones in the service area by deep neural networks (DNN) and fuzzy c-Means (FCM) clustering. The slow time-varying CSI between the base station (BS) and feasible UT locations in the service area is calculated from the geospatial data by offline ray tracing and a DNN-based path estimation model associated with the 1-dimensional convolutional neural network (1D-CNN) and regression tree ensembles. Then, the UT-level CSI of all feasible locations is grouped into clusters by a proposed FCM clustering. Finally, the service area is divided into a number of non-overlapping UT zones. Each UT zone is characterized by a corresponding set of clusters named as UT-group CSI, which is utilized in the analog RF beamformer design of AB-HP to reduce the required large online CSI overhead in the MU-mMIMO systems. Then, the reduced-size online CSI is employed in the baseband (BB) precoder of AB-HP. Simulations are conducted in the indoor scenario at 28 GHz and tested in an AB-HP MU-mMIMO system with a uniform rectangular array (URA) having 16 × 16 = 256 antennas and 22 RF chains. Illustrative results indicate that 91.4% online CSI can be reduced by using the proposed offline channel estimation technique as compared to the conventional online channel sounding. The proposed DNN-based path estimation technique produces same amount of UT-level CSI with runtime reduced by 65.8% as compared to the computationally expensive ray tracing. The imperfection of UT-level CSI introduced by the DNN-based path estimation technique is mitigated by the FCM clustering technique, where the AB-HP using offline UT-group CSI generated by the DNN-based channel estimation model and ray tracing based model for the RF beamformer achieves, respectively, 98.7% and 99.1% sum-rate performance of the fully digital precoding (FDP) technique using full-size online CSI.

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Taxonomy and Performance Evaluation of Hybrid Beamforming for 5G and Beyond Systems

Cited by 34 publications

References 133 publications

Overview of Precoding Techniques for Massive MIMO

Overview of Precoding Techniques for Massive MIMO

Butler Matrix Based Beamforming Networks for Phased Array Antenna Systems: A Comprehensive Review and Future Directions for 5G Applications

A Deep Learning and Geospatial Data-Based Channel Estimation Technique for Hybrid Massive MIMO Systems

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