Massive Antenna Research Articles

Deep Learning-Based Beamforming for Millimeter-Wave Systems Using Parametric ReLU Activation Function

Beamforming design is a crucial stage in millimeter-wave systems with massive antenna arrays. We propose a deep learning network for the design of the precoder and combiner in hybrid architectures. The proposed network employs a parametric rectified linear unit (PReLU) activation function which improves model accuracy with almost no complexity cost compared to other functions. The proposed network accepts practical channel estimation input and can be trained to enhance spectral efficiency considering the hardware limitation of the hybrid design. Simulation shows that the proposed network achieves small performance improvement when compared to the same network with the ReLU activation function.

A General Digital Predistortion Linearization Scheme for Hybrid Beamforming System

Hybrid beamforming multiple-input multiple-output (MIMO) system has attracted a lot of attention in recent years since it significantly reduces the hardware cost compared to the fully digital beamforming system. However, with hybrid beamforming, the one-to-one mapping between the radio frequency (RF) chain and the nonlinear power amplifier (PA) is destroyed. The PA nonlinearity cannot be compensated by conventional digital predistortion (DPD) algorithms. In this article, we propose a novel multiple-input DPD model for hybrid beamforming MIMO systems. An extra digital compensation block is introduced in the feedback path. With the feedback compensation block, the proposed DPD can linearize the PA output signal as well as the aggregated far-field signal. What is more, we propose a DPD estimation algorithm with much reduced complexity. Comparing to existing solutions, the proposed scheme achieves satisfactory performance for hybrid beamforming system. It does not rely on the measurement of the channel or the assumption of massive antennas. Simulation results demonstrate the effectiveness of the proposed DPD scheme.

Energy Efficiency Maximization of Massive MIMO Communications With Dynamic Metasurface Antennas

Future wireless communications are largely inclined to deploy massive numbers of antennas at the base stations (BSs) by leveraging cost- and energy-efficient as well as environmentally friendly antenna arrays. The emerging technology of dynamic metasurface antennas (DMAs) is promising to realize such massive antenna arrays with reduced physical size, hardware cost, and power consumption. The goal of this paper is the optimization of the energy efficiency (EE) performance of DMA-assisted massive multiple-input multiple-output (MIMO) wireless communications. Focusing on the uplink, we propose an algorithmic framework for designing the transmit precoding of each multi-antenna user and the DMA tuning strategy at the BS to maximize the EE performance, considering the availability of either instantaneous or statistical channel state information (CSI). Specifically, the proposed framework is shaped around Dinkelbach's transform, alternating optimization, and deterministic equivalent methods. In addition, we obtain a closed-form solution to the optimal transmit signal directions for the statistical CSI case, which simplifies the corresponding transmission design for the multiple-antenna case. Our numerical results verify the good convergence behavior of the proposed algorithms, and showcase the considerable EE performance gains of the DMA-assisted massive MIMO transmissions over the baseline schemes.

Constellation Design for Multiuser Non-Coherent Massive SIMO Based on DMPSK Modulation

Non-coherent (NC) schemes combined with massive antenna arrays are proposed to replace traditional coherent schemes in scenarios which require an excessive number of reference signals, since NC approaches avoid channel estimation and equalization. Differential <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> -ary phase shift keying is one of the most appealing NC schemes due to its implementation simplicity in realistic scenarios. However, the analytical constellation design for multiuser scenarios is intractable, as discussed in this paper. We propose to solve this problem by using optimization techniques relying on evolutionary computation. We design two approaches, namely Gaussian-approximated optimization and Monte-Carlo based optimization. They can provide both individual constellations for each user equipment and a bit mapping policy to minimize the bit error rate. We perform a complexity analysis and propose strategies for its reduction. We propose a set of constellations for different number of users and constellation sizes, and evaluate the link-level performance of some illustrative examples to verify that our solutions outperforms the existing ones. Finally, we show via simulations that NC outperforms the coherent schemes in high mobility and/or low signal-to-noise ratio scenarios.

Investigation of EMF Exposure Level for Uplink and Downlink of 5G Network Using Ray Tracing Approach

To provide enhanced mobile services, the 5G system is expected to further densify its network infrastructure and scale up the deployment of massive antenna arrays that emit high-energy beams using the millimeter wave spectrum. These radically new

Spectrally Compatible Waveform Design for Large-Scale MIMO Radar Beampattern Synthesis With One-Bit DACs

Multi-input multi-output (MIMO) radar with massive antennas is promising for high resolution applications. However, a big challenge of this system is that the hardware cost and power consumption will increase significantly, if high-resolution quantizers are adopted. In this article, we consider MIMO radar deployed with one-bit digital-to-analog converters, and investigate the problem of designing one-bit transmit sequence with good spatial and spectral properties. Specifically, the one-bit waveform design problem is formulated by minimizing the mean-square error between the desired and designed transmit beampatterns, subject to spectral constraints. The resulting problem, including a nonconvex quartic objective and a nonconvex discrete constraint, is NP-hard, and an alternating optimization (AltOpt) framework with the aid of “almost equivalent” criterion is thereby developed to handle it. Particularly, in the AltOpt framework, a low-complexity algorithm is developed based on the alternating direction method of multipliers approach. Numerical simulations are provided to show the advantages of the proposed method over the state-of-the-art techniques in terms of the spatial and spectral properties as well as computational complexity.

Reconfigurable Refractive Surfaces: An Energy-Efficient Way to Holographic MIMO

Holographic Multiple Input Multiple Output (HMIMO), which integrates massive antenna elements into a compact space to achieve a spatially continuous aperture, plays an important role in future wireless networks. With numerous antenna elements, it is hard to implement the HMIMO via phased arrays due to unacceptable power consumption. To address this issue, reconfigurable refractive surface (RRS) is an energy-efficient enabler of HMIMO since the surface is free of expensive phase shifters. Unlike traditional metasurfaces working as passive relays, the RRS is used as transmit antennas, where the far-field approximation does not hold anymore, urging a new performance analysis framework. In this letter, we first derive the data rate of an RRS-based single-user downlink system, and then compare its power consumption with the phased array. Simulation results verify our analysis and show that the RRS is an energy-efficient way to HMIMO.

A Review of Energy Efficiency and Power Control Schemes in Ultra-Dense Cell-Free Massive MIMO Systems for Sustainable 6G Wireless Communication

The traditional multiple input multiple output (MIMO) systems cannot provide very high Spectral Efficiency (SE), Energy Efficiency (EE), and link reliability, which are critical to guaranteeing the desired Quality of Experience (QoE) in 5G and beyond 5G wireless networks. To bridge this gap, ultra-dense cell-free massive MIMO (UD CF-mMIMO) systems are exploited to boost cell-edge performance and provide ultra-low latency in emerging wireless communication systems. This paper attempts to provide critical insights on high EE operation and power control schemes for maximizing the performance of UD CF-mMIMO systems. First, the recent advances in UD CF-mMIMO systems and the associated models are elaborated. The power consumption model, power consumption parts, and energy maximization techniques are discussed extensively. Further, the various power control optimization techniques are discussed comprehensively. Key findings from this study indicate an unprecedented growth in high-rate demands, leading to a significant increase in energy consumption. Additionally, substantial gains in EE require efficient utilization of optimal energy maximization techniques, green design, and dense deployment of massive antenna arrays. Overall, this review provides an elaborate discussion of the research gaps and proposes several research directions, critical challenges, and useful recommendations for future works in wireless communication systems.

Relay Hybrid Precoding in UAV-Assisted Wideband Millimeter-Wave Massive MIMO System

Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) offers a promising technique to fulfil the high data demand and connectivity of the Internet-of-Things (IoT) and 5G communications because it owns valuable and unknown spectrum resources. Using massive antennas with recently introduced drone-enabled aerial computing platforms, named unmanned aerial vehicles (UAVs), can cast high energy consumption if fully-digital precoding is employed at the UAVs. Using hybrid precoding at a UAV can reduce hardware complexity and energy consumption but is challenging with a need for joint optimization of three precoding matrices at the UAV (sixth-order polynomial objective function). In this paper, we propose to decompose the original UAV hybrid precoding challenge into three subproblems and develop a coordinated descent optimization (CDO) algorithm to solve the three problems recursively. In addition, the convergence and complexity of this new technique are analyzed. Numerical studies indicate the improved effectiveness of the proposed solution over existing solutions.

Secure Multicast Energy-Efficiency Maximization With Massive RISs and Uncertain CSI: First-Order Algorithms and Convergence Analysis

Reconfigurable intelligent surface (RIS) has the potential to significantly enhance the network secure transmission performance by reconfiguring the wireless propagation environment. However, due to the passive nature of eavesdroppers and the cascaded channel brought by the RIS, the eavesdroppers’ channel state information is imperfect at the base station. Under channel uncertainty, the optimal phase-shift, power allocation, and transmission rate design for massive antennas and reflecting elements secure transmission are challenging to solve due to the outage probabilistic constraint with coupled variables. To fill this gap, this paper formulates a problem of energy-efficient secure transmission design with the probabilistic outage constraint. By leveraging the exponential distribution property of the received signal power, the stochastic resource allocation is equivalently transformed into a deterministic one, and the secure energy efficiency maximization problem can be iteratively solved via low complexity first-order algorithms under the alternating maximization (AM) framework. However, due to the nonsmooth problem, the convergence of the objective function value and nature of the converged solution under AM iteration are uncertain. Therefore, the convergence properties with respect to the objective function value and sequence of solutions are further established. Simulation results corroborate the convergence results of the first-order algorithms and show that the proposed algorithm achieves identical performance to the conventional method but saves at least two orders of magnitude in computation time. Moreover, the resultant RIS aided secure transmission significantly improves the energy efficiency compared to baseline schemes of random phase-shift, fixed phase-shift, and RIS ignoring CSI uncertainty.

Two-level-enforced security with hybrid precoding for multicast massive MIMO wiretap systems

Multicast hybrid precoding reaches a compromise among hardware complexity, transmission performance and wireless resource efficiency in massive MIMO systems. However, system security is extremely challenging with the appearance of eavesdroppers. Physical layer security (PLS) is a relatively effective approach to improve transmission and security performance for multicast massive MIMO wiretap systems. In this paper, we consider a transmitter with massive antennas transmits the secret signal to many legitimate users with multiple-antenna, while eavesdroppers attempt to eavesdrop the information. A fractional problem aims at maximizing sum secrecy rate is proposed to optimize secure hybrid precoding in multicast massive MIMO wiretap system. Because the proposed optimized model is an intractable non-convex problem, we equivalently transform the original problem into two suboptimal problems to separately optimize the secure analog precoding and secure digital precoding. Secure analog precoding is achieved by applying singular value decomposition (SVD) of secure channel. Then, employing semidefinite program (SDP), secure digital precoding with fixed secure analog precoding is obtained to ensure quality of service (QoS) of legitimate users and limit QoS of eavesdroppers. Complexity of the proposed SVD-SDP algorithm related to the number of transmitting antennas squared is lower compared with that of constant modulus precoding algorithm (CMPA) which is in connection with that number cubed. Simulation results illustrate that SVD-SDP algorithm brings higher sum secrecy rate than those of CMPA and SVD-SVD algorithm.

FDD LTE Performance Simulation Based on Massive Antenna Array

There are two methodologies to realize 3D MIMO for FDD LTE, MU-MIMO-based virtual beam (VB), and four-cell soft-split cell (SSC), which beamforms four logical cells in one physical sector, and this paper is aimed at figuring out which brings more performance between VB and SSC. In the first place, brief introduction for the mechanism of VB and SSC are presented. Subsequently, detailed systematic simulations based on WINNER II are carried out based on different traffic models (low load, medium load, and high load) and user distribution is also considered. For accurate evaluation on the performance gain of VB and SSC, 4T4R solution is also simulated as the benchmark, which is widely deployed to improve the capacity of FDD LTE by operators. Based on the simulation of results of the subsequent KPIs, cell throughput, average cell throughput, average UE throughput, and average layer number, VB and SSC effectively increase single-cell capacity and user experience in high load scenario compared with 4T3S solution and VB can bring more gain.

Artificial Intelligence for Physical-Layer Design of MIMO Communications with One-Bit ADCs

Wireless systems continue to go toward higher carrier frequencies, including terahertz bands, to take advantage of higher bandwidth channels. At the same time, antenna arrays remain important with continued increases in array elements. However, the power consumption of RF and digital circuits can increase proportionally to both the amount of signal bandwidth and the number of antennas. The use of one-bit analog-to-digital converters at the receiver is a cost- and power-efficient solution for wideband and/or massive antenna wireless systems. The nonlinearity of one-bit received signals brings challenges in physical-layer (PHY) design at the receiver. At the same time, the binary nature of these signals opens new opportunities for artificial intelligence (AI)-based PHY design. This article covers recent progress in incorporating AI into the design of classical PHY techniques and emerging studies on establishing AI-inspired frameworks that fundamentally replace classical model-driven techniques with data-driven AI techniques. It concludes with a discussion, including practical challenges and future research directions.

Wideband hybrid precoder for mmWave multiuser MIMO OFDM communications

Using millimeter wave (mmWave) transmission for massive multiple input multiple output (MIMO) system can improve system performance and effectively reduce the size of the massive antennas array. However, A wideband beamformer design is needed to take advantage of this wideband channel. In this paper, a downlink multi-user massive MIMO orthogonal frequency division multiplexing (MIMO OFDM) system for mmWave communications is proposed. Each subcarrier channel can be approximated as a narrowband clustered channel, so a narrowband precoder can be applied for each subchannel. The hybrid precoder is implemented in a manner so the digital precoder is obtained for each subcarrier, whilst the analog precoder is common for all subcarriers. A modified “joint spatial division/multiplexing” (JSDM) scheme is used to design the precoder, where each user equipped with more than one antenna. The design of the analog precoder is based on the second order channel statistics to reduce the overhead information need to process and fed back and the subcarrier baseband precoder based on the instantaneous channel state information (CSI). Following the approach of Kronecker channel model, the iteration between the analog beamformers design at both end of link can be avoided. Finally testing the system using various numbers of antennas at base station.

CNN-aided multiple PU spectrum sensing in uncalibrated massive antennas SU system

This paper deploys the Convolutional Neural Network (CNN) to learn and set the statistical test in Spectrum Sensing (SS) task of multiple primary user (PU) sources in massive uncalibrated antennas of secondary users (SU) sharing the same spectrum resources. The proposed deep learning-based SS method (DL-SS) is based on the CNN architecture that has the capability of extracting features of the sample covariance matrices (SCMs) that are given as the network input, improving the overall performance and robustness. The proposed CNN-SS method is compared with nine recent multiple-antennas SS methods, namely the arithmetic–geometric detector (AGM), John’s detector (JD), sphericity detector (SD), generalized likelihood test (GLRT), locally most powerful invariant test (LMPIT), maximum–minimum eigenvalue detector (MME), covariance detector (CAV), Hadamard detector (HD) and volume detector (VD) methods; besides, the proposed method is also compared with five recent state-of-art CNN-based SS methodologies. Performance-complexity trade-off of the proposed and reference SS methods are corroborated via Monte Carlo Simulations (MCS). The proposed CNN-SS method under uncalibrated massive antennas reveals substantial benefits w.r.t. the reference methods and is competitive with others CNN-SS methodologies, both in terms of complexity and performance, achieving detection probability of Pd=0.9(@SNR=−20dB) under very low false alarm probability Pf=0.1. Under different figures of merit, the performance of the CNN-based SS detector has revealed to be indubitably superior regarding the state-of-art SS detectors. However, the proposed CNN-based SS detector presents relative computational complexity increases. Hence, to be effective, such a superior operational performance requires a very efficient processing structure in the SU base stations.

Gibbs Sampling Detection for Large MIMO and MTC Uplinks with Adaptive Modulation.

Wireless networks beyond 5G will mostly be serving myriads of sensors and other machine-type communications (MTC), with each device having different requirements in respect to latency, error rate, energy consumption, spectral efficiency or other specifications. Multiple-input multiple-output (MIMO) systems remain a central technology towards 6G, and in cases where massive antenna arrays or cell-free networks are not possible to deploy and only moderately large antenna arrays are allowed, the detection problem at the base-station cannot rely on zero-forcing or matched filters and more complex detection schemes have to be used. The main challenge is to find low complexity, hardware feasible methods that are able to attain near optimal performance. Randomized algorithms based on Gibbs sampling (GS) were proven to perform very close to the optimal detection, even for moderately large antenna arrays, while yielding an acceptable number of operations. However, their performance is highly dependent on the chosen “temperature” parameter (TP). In this paper, we propose and study an optimized variant of the GS method, denoted by triple mixed GS, and where three distinct values for the TP are considered. The method exhibits faster convergence rates than the existing ones in the literature, hence requiring fewer iterations to achieve a target bit error rate. The proposed detector is suitable for symmetric large MIMO systems, however the proposed fixed complexity detector is highly suitable to spectrally efficient adaptively modulated MIMO (AM-MIMO) systems where different types of devices upload information at different bit rates or have different requirements regarding spectral efficiency. The proposed receiver is shown to attain quasi-optimal performance in both scenarios.

Beam division multiple access for millimeter wave massive MIMO: Hybrid zero-forcing beamforming with user selection

&lt;p&gt;Massive multiple-input multiple-output (MIMO) systems are considered a promising solution to minimize multiuser interference (MUI) based on simple precoding techniques with a massive antenna array at a base station (BS). This paper presents a novel approach of beam division multiple access (BDMA) which BS transmit signals to multiusers at the same time via different beams based on hybrid beamforming and user-beam schedule. With the selection of users whose steering vectors are orthogonal to each other, interference between users is significantly improved. While, the efficiency spectrum of proposed scheme reaches to the performance of fully digital solutions, the multiuser interference is considerably reduced.&lt;/p&gt;

Adaptive beamwidth optimization under Doppler ICI and positioning errors at mmWave bands

The growing trends towards massive antenna arrays with focusing capabilities has enabled the use of higher frequencies at the cost of more complex systems. In the particular case of vehicular communications, millimeter-wave (mmWave) communications are expected to unleash a set of advanced use cases with stringent spectrum needs. However, dealing with very directive patterns and high frequencies entail additional challenges such as beam misalignment and Doppler effect. This paper presents a beam optimization procedure for vehicle-to-network (V2N) systems in which a base station communicates with high-speed users. Aided by the a priori knowledge of the vehicle location, the base station is able to estimate the average signal-to-interference-plus-noise ratio (SINR) until the next beam refresh considering the positioning accuracy and the Doppler inter-carrier interference (ICI). The estimation includes the antenna beamwidth, which can be optimized to maximize the achievable throughput. The numerical results indicate that the SINR can be significantly enhanced compared to beam sweeping with identical hierarchical codebooks while reducing the probability of outage.

Radio Sensing Using 5G Signals: Concepts, State of the Art, and Challenges

Radio sensing has become increasingly important, as the demand for “smartness” is drastically increasing. Unlike conventional sensing, radio sensing uses existing radio signals or devices to passively sense the ambient environment for low cost and wide deployment. In this article, a comprehensive overview of radio sensing using the recent fifth-generation (5G) signals is provided. 5G systems have many merits, such as high frequency, large bandwidth, massive antenna array, and dense network, making them ideal for radio sensing. In the overview, basic theories and concepts of 5G radio sensing are first introduced. Then, different state-of-the-art 5G sensing works are discussed based on their applications. These applications show that 5G radio sensing represents a step change in radio sensing. After that, several open challenges in 5G radio sensing are illustrated with relevant insights. These insights manifest that 5G radio sensing has great potentials to explore.

Mutual Decoupling for Massive MIMO Antenna Arrays by Using Triple-Layer Meta-Surface

The high isolation massive antenna arrays are the key devices in the base station of the future wireless communication systems. The challenge for the array application is how to reduce the coupling among the array elements. This paper introduces a new decoupling method for massive MIMO arrays with an innerelement distance of around half wavelength by using a triple-layer metasurface (TMS). The TMS comprises three identical surfaces. The waves reflected from the bottom metasurface (MS) and top two-layer MS have the same amplitudes and the opposite phases, which are canceled with each other. An example of 4×4 dualpolarized wideband microstrip array with a TMS is proposed to verify the decoupling method. The simulation and measurement show that the TMS can help reduce the coupling among the array elements to less than -24 dB in both the co-polarization and cross-polarization directions within 4.23-4.82 GHz. In the meanwhile, the radiation characteristics of the arrays with and without TMS are almost unchanged.