Analog Precoding Research Articles

Multiple Beam Selection and Near-Optimal Digital Precoding for Multiuser Millimeter-Wave Massive MIMO Systems

Beamspace massive multiple-input multiple-output (MIMO) can reduce the number of required radio-frequency (RF) chains without obvious performance loss. However, the joint power leakage and multiuser interferences (MI) problem in beamspace channels degrades the system performance seriously, which is not considered in most recent works. To solve this problem, we propose a hybrid analog/digital precoding scheme with multiple beam selection. First, all the energy focusing beams are selected unshared to collect the channel power and alleviate the MI. Then, the analog precoding is determined by maximizing the array gains for each user. The digital precoding is designed by maximizing the system sum rate under the power constraint. However, the logarithms and fractions in the objective function make the optimization problem nonconvex. Hence, we use the Lagrangian transform to eliminate the logarithms, as well as fractional programming to remove the fractions. Finally, the digital precoding matrix is obtained via convex optimization. Simulation results verify that the proposed scheme can obtain the considerable sum rate performance, while achieving a higher energy efficiency than those of existing schemes.

Joint hybrid-precoding design for MU-MISO systems with a subconnected architecture

In this study, we propose a joint hybrid-precoding algorithm for multiuser multiple-input single-output downlink systems. Specifically, we consider that the base station employs an energy-efficient hybrid-precoding subconnected (SC) architecture with fixed equal subarrays (FESA) (SC-FESA). Optimizing the analog precoding matrix in an SC-FESA architecture is challenging due to its unique constraint structure. In this study, to maximize system sum rate, we propose an efficient method to transform the system’s sum-rate optimization problem into a continuous and differentiable objective function wherein only the nonzero elements of the analog precoding matrix are optimized. For the formulated problem, we develop an alternating optimization (AO) approach to jointly optimize the digital and analog precoders in succession by maximizing the system’s sum rate. Specifically, in the proposed AO method, when the digital precoder is fixed, we employ the Riemannian conjugate gradient algorithm to generate the analog precoder. Furthermore, when the analog precoder is fixed, we use the minimum mean squared error method to obtain the digital precoder. Numerical simulation results show that the proposed AO algorithm improves the sum rate and energy efficiency of the SC-FESA architecture compared to existing algorithms.

Deep Learning-Based Hybrid Precoding for Terahertz Massive MIMO Communication With Beam Squint

In this letter, a wideband hybrid precoding network (WHPC-Net) based on deep learning is designed for Terahertz (THz) massive multiple input multiple output (MIMO) system in the face of beam squint. Firstly, the channel state information (CSI) is preprocessed by calculating the mean channel covariance matrix (MCCM). Next, the analog precoder can be calculated based on the analog precoding sub-network (APC-Net) using the information of the MCCM. Finally, the digital precoder will be obtained with the aid of the digital precoding subnetwork (DPC-Net), employing the related outputs of the APC-Net and the MCCM. Simulation results show that the proposed WHPC-Net is more robust to the beam squint over the existing traditional hybrid precoders. For the case of imperfect CSI, the proposed WHPC-Net even is capable of achieving a higher sum rate than the full-digital precoder based on singular value decomposition.

Hybrid Beamforming for Dual Functioning Multi-input Multi-output Radar using Dimension Reduced-baseband Piecewise Successive Approximation

A reliable and effective hybrid beamforming design for dual functioning multi-input multi-output (MIMO) radar is a challenging research problem because of the concerns related to limited user capacity, interference, and lack of performance trade-off. Due to the shortage of available spectrum, radar frequency spectrum sharing has become vital in emerging 5G communication systems. This will reduce spectrum congestion, therefore receiving significant attention. The existing hybrid beamforming methods reduce the radio frequency (RF) chains but improving user capacity is still a major concern. Future dual radar-communication designs are having challenges in enhancing the user capacity with minimum RF chains, interference mitigation, and hardware cost reduction. This work proposes a novel approach to a hybrid beamforming mechanism for dual-functioning MIMO radar. This mechanism uses the dimension-reduced baseband piecewise successive approximation integrated with a digital precoder. At the analog precoder, the piecewise successive iterative approximation approach is applied to perform the analog beamforming. The novel hybrid beamforming with lens antenna array integration improves the user capacity and reduces power requirement, interference, and expenses. The simulation results showed improved performances compared to existing state-of-the-art methods in terms of bit error rate, spectral efficiency, energy efficiency, and response time.

Modified User Grouping and Hybrid Precoding for Information Decoding and Energy Harvesting in Hardware Impaired Point-To-Point MM-Wave Massive MIMO NOMA

The fifth generation (5G) and beyond 5G race covers both spectral-efficient and energy-efficient mechanisms such as simultaneous wireless information decoding and energy harvesting (SWIDEH). Structured as full or sub-connection of hybrid-precoded mm-wave massive multiple-input multiple-output non-orthogonal multiple access (NOMA) systems. However, the more practical hardware-impaired (HI) functionality yet to be considered for point-to-point (p-to-p) MIMO is conceived from an existing system. A hardware impairment-based power splitting is proposed for energy harvesting. The energy of the HI signal is harnessed through the joint optimization of power allocation (PA) for information decoding (ID) and the power splitting (PS) factor for energy harvesting (EH) using zero-forcing (ZF) precoding and minimum mean square error (MMSE) downlink (DL) detection. Based on the simulation results, an adaptive cluster head selection (CHS) criterion is proposed for analog precoding to mitigate intracluster interferences at a high signal-to-noise ratio (SINR). A successive interference cancellation (SIC) is carried out to enable dynamic channel overhead estimation and user signal detection. An investigation is done by simulating the system performance at varied numbers of users, the number of radio-frequency (RF) chains, and channel characteristics for modelling the user group threshold coupled with channel size reduction via an antenna selection scheme. Observing the execution time and some graphical plots shows that the spectral and energy efficiency can be improved by a random (R) or maximum norm average (MNA) channel vector formation from each user’s channel matrix combined with the best adaptive correlation threshold during CHS and a regularized ZF (RZF) precoder.

Secrecy Rate Optimization in Nonlinear Energy Harvesting Model-Based mmWave IoT Systems With SWIPT

Secrecy rate (SR) optimization in millimeter wave (mmWave) Internet of Things (IoT) systems with simultaneous wireless information and power transfer (SWIPT) is studied in this article. Adopting the SWIPT architecture, energy-constrained devices get charged by the radio-frequency waves transmitted from a base station. The hybrid precoding technique is applied to reduce the implementation cost by separately designing a digital precoder and an analog precoder. Also, we adopt the artificial noise (AN)-assisted transmission method to maximize the SR. In this problem, we aim to jointly optimize the digital precoding vector, AN covariance matrix, and power-splitting ratio under the nonlinear energy harvesting (EH)-constraints. Then, we propose a semidefinite relaxation-based alternating optimization algorithm for the case of perfect channel state information (CSI) and imperfect CSI. Finally, simulation results show that the proposed algorithms are effective to improve the SR.

CNN Based Hybrid Precoding for MmWave MIMO Systems With Adaptive Switching Module and Phase Modulation Array

Hybrid precoding is considered as a promising candidate to balance between the sum rate and the power and cost in the mmWave MIMO systems. However, it still suffers from huge power consumption due to considerable radio frequency links and phase shifters. In this study, an energy-efficient adaptive switching module (ASM) and phase modulation array (PMA) assisted hybrid precoding scheme is proposed, in which ASM is introduced to enhance the energy efficiency while PMA is employed to implement analog precoding to reduce power consumption. The ASM-PMA hybrid precoder design problem related to three matrix variables is formulated. Besides, the joint ASM-PMA hybrid precoder/combiner design at both transmitter and receiver is also investigated. Owing to the tough task of joint optimization of matrix variables, we can divide it into two sub-problems. Correspondingly, alternating optimization hybrid precoding (AOHP) algorithm is suggested to solve the PMA. Then, matching theory and convolutional neural network (CNN) based algorithms are respectively proposed to seek the optimal ASM. For the CNN based scheme, relaxed adaptive switching CNN (RAS-CNN) is designed to solve the binary integer problem in the offline train stage, while in the online deployment stage, ideal adaptive switching CNN (IAS-CNN) is adopted. Furthermore, CNN accepts estimated channel as input to yield robust precoding performance and lower computational complexity. Simulation results verify that, the proposed hybrid precoding can outperform other schemes in energy efficiency with satisfactory spectral efficiency.

Deep Learning for Channel Sensing and Hybrid Precoding in TDD Massive MIMO OFDM Systems

This paper proposes a deep learning approach to channel sensing and downlink hybrid beamforming for massive multiple-input multiple-output systems operating in the time division duplex mode and employing either single-carrier or multicarrier transmission. The conventional precoding design involves a two-step process of first estimating the high-dimensional channel, then designing the precoders based on such estimate. This two-step process is, however, not necessarily optimal. This paper shows that by using a learning approach to design the analog sensing and the hybrid downlink precoders directly from the received pilots without the intermediate high-dimensional channel estimation, the overall system performance can be significantly improved. Training a neural network to design the analog and digital precoders simultaneously is, however, difficult. Further, such an approach is not generalizable to systems with different number of users. In this paper, we develop a simplified and generalizable approach that learns the uplink sensing matrix and downlink analog precoder using a deep neural network that decomposes on a per-user basis, then designs the digital precoder based on the estimated low-dimensional equivalent channel. Numerical comparisons show that the proposed methodology results in significantly less training overhead and leads to an architecture that generalizes to various system settings.

Deep frameworks based on residual dense network and reinforcement learning for mmWave massive MIMO systems

SummaryIn millimeter wave (mmWave) massive multiple‐input‐multiple‐output (MIMO) systems, hybrid precoding plays a pivotal role in reducing complexity and cost while providing a good spectral efficiency. However, implementation of digital precoders with large number of antennas is difficult due to hardware constraints, while analog precoders offer confined performance. This leads to high computational complexity and cannot fully exploit the spatial information. Previous studies on hybrid precoding were based on exhaustive search solutions or greedy schemes, which result in higher complexity system performance. To face these challenges, this paper proposes deep hybrid precoding framework with phase quantization and residual dense network to design the matrix of analog and digital precoders. The proposed deep hybrid precoding technique consists of offline training stage and online deployment stage. In offline training stage, hybrid precoding is obtained assuming the approximate phase quantization. While in the online deployment stage, the matrix of analog precoding is calculated by exchanging approximate phase quantization with ideal phase and grouping the analog precoding vectors. In this paper, we also propose a deep reinforcement learning‐based hybrid precoding. It consists of a deep reinforcement learning with employing convolutional neural network and long short‐term memory (LSTM) methods. In our proposed frameworks, structures of proposed techniques are trained for maximum spectral efficiency. Our proposed techniques are compared with other precoding techniques. Results illustrate that the proposed techniques outperforms the other precoding techniques in terms of the spectral efficiency.

Design of Hybrid Precoding for Millimeter-Wave MIMO-NOMA Systems Based on User Fairness

This letter studies the design of hybrid precoding in millimeter-Wave (mmWave) multiple input multiple output-non orthogonal multiple access (MIMO-NOMA) systems. To guarantee the rate performance for all served users, we formulate the optimization problem of joint analog precoding (AP) and digital precoding (DP) vectors based on maximum and minimum (Max-Min) fairness to maximize the minimal achievable rate of users, i.e., we design different HP vectors for each user from the perspective of fairness. This can be solved by the proposed two-stage optimization scheme. First, the AP matrix and the DP vectors of each user are alternately designed, where the AP and DP vectors can be obtained by using the Semi-Definite Relaxation algorithm and the proposed boundary-compressed particle swarm optimization (BC-PSO) algorithm, respectively. Then, the optimal scaling factors obtained based on the Karush-Kuhn-Tucker (KKT) condition are introduced to further to improve the Max-Min rate. Simulation results show that the proposed scheme performs better than existing schemes.

RIS-Assisted Robust Hybrid Beamforming Against Simultaneous Jamming and Eavesdropping Attacks

Wireless communications are increasingly vulnerable to simultaneous jamming and eavesdropping attacks due to the inherent broadcast nature of wireless channels. With this focus, due to the potential of reconfigurable intelligent surface (RIS) in substantially saving power consumption and boosting information security, this paper is the first work to investigate the effect of the RIS-assisted wireless transmitter in improving both the spectrum efficiency and the security of multi-user cellular network. Specifically, with the imperfect angular channel state information (CSI), we aim to address the worst-case sum rate maximization problem by jointly designing the receive decoder at the users, both the digital precoder and the artificial noise (AN) at the base station (BS), and the analog precoder at the RIS, while meeting the minimum achievable rate constraint, the maximum wiretap rate requirement, and the maximum power constraint. To address the non-convexity of the formulated problem, we first propose an alternative optimization (AO) method to obtain an efficient solution. In particular, a heuristic scheme is proposed to convert the imperfect angular CSI into a robust one and facilitate the developing a closed-form solution to the receive decoder. Then, after reformulating the original problem into a tractable one by exploiting the majorization-minimization (MM) method, the digital precoder and AN can be addressed by the quadratically constrained quadratic programming (QCQP), and the RIS-aided analog precoder is solved by the proposed price mechanism-based Riemannian manifold optimization (RMO). To further reduce the computational complexity of the proposed AO method and gain more insights, we develop a low-complexity monotonic optimization algorithm combined with the dual method (MO-dual) to identify the closed-form solution. Numerical simulations using realistic RIS and communication models demonstrate the superiority and validity of our proposed schemes over the existing benchmark schemes.

A Joint Hybrid Precoding/Combining Scheme Based on Equivalent Channel for Massive MIMO Systems

Due to its inherent ability in reducing hardware cost and power consumption while maintaining high system capacity, hybrid precoding is deemed as one of the key technologies in the upcoming 5G/6G millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. However, it is challenging to design high performance hybrid precoders/combiners with low computational complexity. In this paper, based on the singular value decomposition (SVD) technique and the concept of equivalent channel, joint hybrid precoding strategies with high spectral-efficiency and low complexity are proposed for both single-user and multi-user massive MIMO systems. Specifically, for single-user massive MIMO scenarios, after transforming the design of hybrid beamforming into the problem of maximizing the square of sum eigenvalues for an equivalent channel, a two-stage successive method is conceived to design the analog precoder and combiner jointly, and the corresponding equivalent channel is constructed. Then, the digital precoding and combining operations are realized directly by applying the SVD technique to the matrix of equivalent channel. Meanwhile, the hybrid precoding strategy is extended to the multi-user scenario for achieving high performance resultant from multi-user diversity. Extensive simulations are conducted to verify the effectiveness of the precoding/combing schemes. The results show that our proposed schemes can achieve superior performance with lower complexity compared to the existing ones.

Cross-Layer Optimization for Industrial Internet of Things in NOMA-Based C-RANs

This article investigates nonorthogonal multiple access (NOMA)-based cloud radio access networks (C-RANs), where edge caching is adopted to cut down the crowdedness of the fronthaul links. We aim to maximize the energy efficiency (EE) by jointly optimizing the power allocation, analog, and digital precoding, which turns out to be an intractable nonconvex optimization problem. To tackle this problem, we first select cluster heads using the selecting cluster-head (SCH) algorithm, where the analog precoding matrix can be resolved by means of maximizing the array gains. Then, the device grouping algorithm is proposed to group devices according to the equivalent channel correlations, and thus, the NOMA devices in the same beam are capable of sharing the same digital precoding vector. Finally, the joint digital precoding design and power allocation algorithm is proposed to decompose the resultant optimization problem into two subproblems and solve them iteratively by applying the Taylor expansion operation and the minimum mean square error (MMSE) detection. Simulation results validate that the proposed NOMA-based C-RANs with a hybrid precoding (HP) scheme can achieve higher spectral efficiency and EE than the traditional orthogonal multiple access (OMA)-based approach and two-stage HP scheme.

MDL-AltMin: A Hybrid Precoding Scheme for mmWave Systems With Deep Learning and Alternate Optimization

The hybrid precoding structure composed of analog and digital precoders has received increasing attention in mmWave massive <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multiple-input multiple-output</i> (MIMO) systems because it can balance the energy consumption and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">spectral efficiency</i> (SE). However, it is challenging to obtain the optimal hybrid precoding scheme by joint optimization with lower computational complexity. This letter proposes a hybrid precoding scheme based on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">model-driven deep learning and alternate minimization</i> (MDL-AltMin), which is implemented by alternately solving analog precoder and digital precoder. During the alternation, we design an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">analog precoding network</i> (AP-Net) to solve the phase shift network in analog precoder with the goal of maximizing SE. The digital precoder is solved by the Lagrange multiplier method. In each alternate optimization process, the criteria for convergence is to minimize the error between the hybrid precoder and the optimal fully digital precoder. The simulation results show that the SE of our proposed scheme is very close to the fully digital precoding scheme based on singular value decomposition with lower computational complexity.

Multi-IRS-Assisted mmWave MIMO Communication Using Twin-Timescale Channel State Information

To reduce the computational complexity and channel estimation overhead for multi-intelligent reflecting surface (IRS)-assisted millimeter wave (mmWave) multiple-input multiple-output (MIMO) communication, we consider a joint design of the hybrid precoders at the base station and the passive precoders at the IRSs to maximize the ergodic spectral efficiency by exploiting the twin-timescale channel state information (CSI). Specifically, the digital precoder is designed according to the instantaneous CSI of a reduced-dimensional assist channel matrix, while the IRS passive reflection coefficient matrices and the analog precoder are optimized using the statistical CSI of all links. However, such a design problem is challenging to solve due to the non-convexity and the twin timescale. This work proposes efficient algorithms to jointly design the precoders, where the update of the IRS reflection coefficient matrices is independent of the hybrid precoders and the design of the analog precoder is independent of the digital precoder. Simulation results demonstrate the effectiveness of the proposed algorithms and provide the application scenes of the fully-connected and subarray-connected architectures. The results also show that the ergodic spectral efficiency for the fully-connected architecture using the twin-timescale CSI can approach that using the existing CSI schemes with less channel estimation overhead and computational complexity.

Estimation of Channel for Millimeter-Wave Hybrid Massive MIMO Systems using Orthogonal Matching Pursuit (OMP)

In a millimeter-wave (mm-Wave) large MIMO system, hybrid precoding is a critical component for lowering radio-frequency hardware costs as compared to the traditional full-digital precoding strategy. Knowledge of channels is required for hybrid precoding. Though, estimation of the channel is problematic for the mm-Wave system because of the usage of a massive array of antenna and hybrid architecture with analog precoding. Due to the extremely directed nature of wireless propagation, wireless channels have spatial sparsity. In this paper, we exploit this spatial sparsity nature of wireless channels to develop orthogonal matching pursuit technique-based estimation of channels for hybrid millimeter-wave (mm-Wave) wireless systems. Genie (Ideal) channel estimation is also performed in which we presume that actual angle of departures (AoD) and angle of arrivals (AoA) are known to us. Finally, the simulation results reveal that suggested OMP algorithm-based channel estimation has a significant advantage over conventional approaches like least-squares channel estimation.

Massive MIMO Hybrid Precoding for LEO Satellite Communications With Twin-Resolution Phase Shifters and Nonlinear Power Amplifiers

The massive multiple-input multiple-output (MIMO) transmission technology has recently attracted much attention in the non-geostationary, e.g., low earth orbit (LEO) satellite communication (SATCOM) systems since it can significantly improve the energy efficiency (EE) and spectral efficiency. In this work, we develop a hybrid analog/digital precoding technique in the massive MIMO LEO SATCOM downlink, which reduces the onboard hardware complexity and power consumption. In the proposed scheme, the analog precoder is implemented via a more practical twin-resolution phase shifting (TRPS) network to make a meticulous tradeoff between the power consumption and array gain. In addition, we consider and study the impact of the distortion effect of the nonlinear power amplifiers (NPAs) in the system design. By jointly considering all the above factors, we propose an efficient algorithmic approach for the TRPS-based hybrid precoding problem with NPAs. Numerical results show the EE gains considering the nonlinear distortion and the performance superiority of the proposed TRPS-based hybrid precoding scheme over the baselines.

A Highly Pattern-Reconfigurable Planar Antenna With 360° Single- and Multi-Beam Steering

A novel single-fed horizontally polarized pattern-reconfigurable antenna is proposed with the key feature that is highly pattern-reconfigurable. The antenna is able to generate a large variety of radiation patterns, including omnidirectional, single-directive, and multi-beam patterns without using phase shifters. The antenna is based on a planar geometry, which consists of an Alford loop and four compact parasitic pixel rings with 60 integrated controlling units utilizing p-i-n diodes. Experimental and simulation results are provided for the proposed antenna design operating at 5 GHz. The results show that the single-beam steering with 7.3 dBi gain through 360° in the azimuth plane is achieved. Moreover, flexible dual-beam steering with one beam fixed and the other beam scanning independently among 360° in the azimuth plane is also demonstrated where each beam has 5 dBi gain. Other radiation modes, such as the omnidirectional mode, the tri-beam-steering mode, and the elevation plane steering mode, are also verified. The features of high pattern reconfigurability, 360° single-beam- and multi-beam-steering ability, full 3-D space scanning, planar geometry, and compatibility with a digital controller make it useful in various sub-6 GHz wireless applications, such as wireless power transfer, RF sensing, and analog precoding.

Hybrid Analog/Digital Precoding for Downlink Massive MIMO LEO Satellite Communications

Massive multiple-input multiple-output (MIMO) is promising for low earth orbit (LEO) satellite communications due to the potential in enhancing the spectral efficiency. However, the conventional fully digital precoding architectures might lead to high implementation complexity and energy consumption. In this paper, hybrid analog/digital precoding solutions are developed for the downlink operation in LEO massive MIMO satellite communications, by exploiting the slow-varying statistical channel state information (CSI) at the transmitter. First, we formulate the hybrid precoder design as an energy efficiency (EE) maximization problem by considering both the continuous and discrete phase shift networks for implementing the analog precoder. The cases of both the fully and the partially connected architectures are considered. Since the EE optimization problem is nonconvex, it is in general difficult to solve. To make the EE maximization problem tractable, we apply a closed-form tight upper bound to approximate the ergodic rate. Then, we develop an efficient algorithm to obtain the fully digital precoders. Based on which, we further develop two different efficient algorithmic solutions to compute the hybrid precoders for the fully and the partially connected architectures, respectively. Simulation results show that the proposed approaches achieve significant EE performance gains over the existing baselines, especially when the discrete phase shift network is employed for analog precoding.

Distributed Hybrid Equalization for Cooperative Millimeter-Wave Cell-Free Massive MIMO

The current cellular systems are conventional cell-centric based architectures, and thus the inter-cell interference greatly affects the quality of service (QoS) of users located at the border of those service areas. For that reason, future wireless systems will rely on a cell-less deployment by using an alternative promising network infrastructure known as cell-free (CF) technology. Here, in contrast to the current networks, there are no concepts of cells or cell boundaries since it is a disruptive paradigm where a large number of geographically distributed access points (APs) simultaneously and jointly serve a much smaller number of user terminals (UTs) to fulfill the exponentially increasing number of users. Therefore, this paper aims to contribute on this field and considers the design of hybrid analog/digital multi-user equalizer for broadband millimeter wave (mmWave) CF massive multiple-input multiple-output (mMIMO) systems, using a distributed beamforming approach (processing is distributed by APs and central unit (CU)) to reduce the fronthaul requirements. First, only a low complexity analog precoder is applied to the transmitter, while a joint design of APs hybrid equalizer and CU combiner are considered by minimizing the average mean square error (MSE) between the transmitted and CU estimated signals. To achieve this joint design, a cyclic minimization method is used to iterate between APs hybrid equalizer and CU combiner, where the information is exchanged between APs and CU based on a set of error variances to improve detection performance. The simulation results show that the proposed precoder and the hybrid linear equalizer can achieve performance results very close to those of the fully digital system. Moreover, just two iterations of the proposed algorithm are already enough to match the full centralized approach performance.