Robust Hybrid Analog/Digital Beamforming for Uplink Massive-MIMO with Imperfect CSI

Morsali, Alireza; Champagne, Benoı̂t

doi:10.1109/wcnc.2019.8886103

Cited by 15 publications

(11 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A model for the uncertainty of the CSI can be developed based on a precoder to minimize the transmission power subject to SINR constraints for all channel sets [55], [62]. In [63], a design of robust hybrid analog/digital beamforming systems under imperfect CSI was proposed where a norm-bounded channel error and the MMSE were utilized to capture the imperfect CSI conditions. In [64], hardware cost and power consumption of a hybrid analog/digital beamforming systems have been lowered by a reduction of training sequence dimension where a limited number of RF chains were utilized.…”

Section: Signal Processing Challengesmentioning

confidence: 99%

Overview of Precoding Techniques for Massive MIMO

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

Section: Signal Processing Challengesmentioning

confidence: 99%

Overview of Precoding Techniques for Massive MIMO

et al. 2021

View full text Add to dashboard Cite

show abstract

“…We assume N RF = K , as it is the minimum possible number of RF chains for linearly decoding the received signal [5].…”

Section: System Modelmentioning

confidence: 99%

“…The authors of [4] propose a decoupled two‐stage hybrid beamforming design for multi‐user MIMO systems using eigenvectors based analog precoder and combiner to maximize the energy efficiency. It uses singular value decomposition based analog combiner [5, (37)] in the case of multiuser uplink of a mmWave massive‐MIMO base station (BS). A heuristic hybrid precoding design for the fully connected and the partially connected structures aiming to maximize the weighted sum rate in the downlink using practical number of RF chains is proposed in [6].…”

Section: Introductionmentioning

confidence: 99%

A low complexity hybrid combiner design for the ill‐conditioned multiuser mmWave massive MIMO uplink

Ahmed¹,

Khammari²,

Alraddady

2021

IET Communications

View full text Add to dashboard Cite

This paper presents a simple and robust hybrid combiner for the multiuser massive multiple‐input multiple‐output (MIMO) uplink. The hybrid beamforming is used in massive MIMO systems to reduce the number of radio frequency chains. Radio frequency chain consists of power hungry electronic components like mixer, analog‐to‐digital converter / digital‐to‐analog converter etc. This paper proposes a Householder based hybrid beamforming (HHBF) design to simultaneously reduce the beam domain inter‐user interference and increase the sum‐rate performance of massive MIMO uplink. The Householder reflectors generate the orthogonal analog combiner matrix which not only reduces inter‐user interference but also helps reduce the dimension of the digital combiner. It has been shown that the proposed HHBF scheme provides better sum‐rate performance than the Gram–Schmidt based hybrid beamforming design in the real‐World ill‐conditioned MIMO channel. Simulation results show that the beam domain orthogonality error remains less than 0.5 with 0.97 probability and the proposed HHBF provides 25.95% increased sum‐rate at 10 dB SNR.

show abstract

“…DRAFT This approach can be applied in principal to a variety of non-linear beamforming technique such as vector perturbation [55], Tomlinson-Harashima precoding [56], [57], robust beamformer design under imperfect CSI [21], and space-time coding [58]. In the next section, we design and train HDNNs with F T (s) selected as the eigen-mode beamforming for both uplink and downlink.…”

Section: B Dl-based Transceiver Designmentioning

confidence: 99%

Deep Learning Framework for Hybrid Analog-Digital Signal Processing in mmWave Massive-MIMO Systems

Morsali,

Haghighat,

Champagne

2021

Preprint

Self Cite

View full text Add to dashboard Cite

Hybrid analog-digital signal processing (HSP) is an enabling technology to harvest the potential of millimeter-wave (mmWave) massive-MIMO communications. In this paper, we present a general deep learning (DL) framework for efficient design and implementation of HSP-based massive-MIMO systems.Exploiting the fact that any complex matrix can be written as a scaled sum of two matrices with unitmodulus entries, a novel analog deep neural network (ADNN) structure is first developed which can be implemented with common radio frequency (RF) components. This structure is then embedded into an extended hybrid analog-digital deep neural network (HDNN) architecture which facilitates the implementation of mmWave massive-MIMO systems while improving their performance. In particular, the proposed HDNN architecture enables HSP-based massive-MIMO transceivers to approximate any desired transmitter and receiver mapping with arbitrary precision. To demonstrate the capabilities of the proposed DL framework, we present a new HDNN-based beamformer design that can achieve the same performance as fully-digital beamforming, with reduced number of RF chains. Finally, simulation results are presented confirming the superiority of the proposed HDNN design over existing hybrid beamforming schemes.

show abstract

Robust Hybrid Analog/Digital Beamforming for Uplink Massive-MIMO with Imperfect CSI

Cited by 15 publications

References 19 publications

Overview of Precoding Techniques for Massive MIMO

Overview of Precoding Techniques for Massive MIMO

A low complexity hybrid combiner design for the ill‐conditioned multiuser mmWave massive MIMO uplink

Deep Learning Framework for Hybrid Analog-Digital Signal Processing in mmWave Massive-MIMO Systems

Contact Info

Product

Resources

About