Massive MIMO Research Articles

Machine learning-inspired hybrid precoding with low-resolution phase shifters for intelligent reflecting surface (IRS) massive MIMO systems with limited RF chains

Machine learning-inspired hybrid precoding with low-resolution phase shifters for intelligent reflecting surface (IRS) massive MIMO systems with limited RF chains

Outdoor-to-Indoor mmWave Relaying with Massive MIMO: Impact of Imperfect Channel Estimation

Assuming incomplete knowledge of the channel state information (CSI), we investigate two scenarios involving millimeter wave (mmWave) relaying to support outdoor-to-indoor communications. We proceed to derive the average signal-to-noise ratio (SNR) expressions for two relaying scenarios and quantify the asymptotic SNR. The performance of the two relaying scenarios is evaluated using the outage probability—for which we have derived closed-form equations—the end-to-end channel capacity, and the energy efficiency. The obtained results are compared with those derived assuming complete knowledge of the CSI. The effect of the imperfect CSI is therefore assessed in relation to the reference of perfect CSI. In these scenarios, an outside base station (BS) in an urban cellular network serves several indoor users. In the context of a two-hop full-duplex (FD) relaying scheme, we initially suggest a method in which the base station (BS) utilizes zero-forcing (ZF) precoding, and we take into account the overall channel response. Furthermore, we make the assumption that the base station (BS) engages in precoding only depending on the response of the channel in the first hop; in this second design, the relay precodes (using the response of the second-hop channel), amplifies, and sends the signals. Both techniques utilize massive multiple-input–multiple-output (mMIMO) arrays to permit transmission. We also present Monte Carlo simulation results to assess the accuracy of our analytical results. Finally, the two systems are compared in terms of channel estimation and precoding complexity, the number of antennas, as well as the number of users. Practical deployment recommendations are formulated at the end of this work.

Optical TTD compensation network-based phase precoding for THz massive MIMO systems

In sixth generation (6G) communications, terahertz (THz) communication is one of the most important technologies in the future due to its ultra-bandwidth, where hybrid beamforming has been widely used to solve the severe transmission attenuation in the THz band. However, the use of frequency-flat phase shifters in hybrid beamforming leads to the beam split effect. To solve the beam split influence, we propose a novel optical true time delay compensation network (OTTDCN)-based phase precoding structure with low power consumption. In the proposed scheme, the OTTDCN pre-generates multiple beam compensation modes to achieve phase compensation for different frequencies. As a result, the compensated beams can be reoriented toward the target direction at different frequencies. Moreover, a low-complexity beam compensation mode-based hybrid precoding algorithm is proposed, where the selection of the optimal beam compensation modes used for all radio-frequency (RF) chains with finite beam compensation modes is considered. The results show that the OTTDCN-based phase precoding scheme can effectively alleviate the beam split effect with low power consumption and achieve near-optimal performance.

An Analytical Reliability Performance Assessment of Cell-Free Massive MIMO Systems for 5G and Beyond Cellular Networks

In today‟s realm, the 5G cellular networks offer a new era of fully digital environment by connecting people anywhere anytime. This era requires inspiring demands to the networks in terms of spectral and energy efficiencies, low-latency and ultra-reliable communications. A major development to deal with these unparalleled demands is the introduction of Cell- Free Massive MIMO (CF mMIMO) systems to the cellular network. In recent years, mathematical foundations have been laid for a CF mMIMO communication system to evaluate the spectral and energy efficiencies given the number of access points. Various existing literatures have adopted mathematical models to formulate closed-form expressions for these efficiencies using uncorrelated Rayleigh fading for simple linear processing detectors. In this paper, we try to address a compelling and interesting question which has not received much attention lately. The question is how to evaluate the reliability of a connected user terminal in both a CF mMIMO and conventional mMIMO system? We answer this question by first analyzing the signal models for both systems assuming exact channel estimates and consider an uncorrelated Rayleigh fading channel model. The exact Bit Error Rate, as a reliability assessment, in closed-form are then derived based on strong mathematical foundations, which are simple to numerically evaluate in Matlab R2022, for MPSK and MQAM modulation schemes. The mathematical models presented in this work can be used in 5G and Beyond 5G networks to evaluate the distributions of error rates for a user given the number of Access points or Base Station antennas and modulation scheme. Machine Learning algorithms can be applied to the analytical expressions with a view to predict the error performance of a user terminal given the fading characteristic of the propagation environmen

3D Antenna array for multi-user massive MIMO system to minimize unfavorable propagation

Unfavorable propagation in massive multiple input multiple output (MIMO) systems is an underlying channel condition that reduces the gains offered by the system in terms of parallel channels. This paper presents a study on different types of antenna array configurations at base station (BS) to minimize unfavorable propagation conditions. Unfavorable propagation results in increased correlation between channel vectors of two or more mobile terminals (MTs) which arises when the signals from two or more MTs have small angular separation and small angular spread. The study starts with the investigation of the conventional uniform linear array (ULA) under different propagation conditions using a geometrical channel model based on a single cluster of scatterers per MT to quantify the performance degradation in unfavorable propagation. Subsequently, the performance of other types of array geometries is investigated. A custom cluster-based geometrical channel model is used in this study which includes practical propagation aspects like multipath fading, shadow fading, and the effect of BS height. Further, the study considers two cases: micro-cell and macro-cell. Under space constraint settings in terms of the maximum dimension of the array, the proposed technique uses randomized antenna locations in the array to minimize the effect of unfavorable propagation. Based on the results on performance parameters like user correlation and capacity, the study reveals that an antenna array configuration comprising of elements distributed random-homogeneously in a spherical shell outperforms other configurations.

Dual-Driven Learning-Based Multiple-Input Multiple-Output Signal Detection for Unmanned Aerial Vehicle Air-to-Ground Communications

Unmanned aerial vehicle (UAV) air-to-ground (AG) communication plays a critical role in the evolving space–air–ground integrated network of the upcoming sixth-generation cellular network (6G). The integration of massive multiple-input multiple-output (MIMO) systems has become essential for ensuring optimal performing communication technologies. This article presents a novel dual-driven learning-based network for millimeter-wave (mm-wave) massive MIMO symbol detection of UAV AG communications. Our main contribution is that the proposed approach combines a data-driven symbol-correction network with a model-driven orthogonal approximate message passing network (OAMP-Net). Through joint training, the dual-driven network reduces symbol detection errors propagated through each iteration of the model-driven OAMP-Net. The numerical results demonstrate the superiority of the dual-driven detector over the conventional minimum mean square error (MMSE), orthogonal approximate message passing (OAMP), and OAMP-Net detectors at various noise powers and channel estimation errors. The dual-driven MIMO detector exhibits a 2–3 dB lower signal-to-noise ratio (SNR) requirement compared to the MMSE and OAMP-Net detectors to achieve a bit error rate (BER) of 1×10−2 when the channel estimation error is −30 dB. Moreover, the dual-driven MIMO detector exhibits an increased tolerance to channel estimation errors by 2–3 dB to achieve a BER of 1×10−3.

SEAttention-residual based channel estimation for mmWave massive MIMO systems in IoV scenarios

To improve the accuracy and efficiency of time-varying channels estimation algorithms for millimeter Wave (mmWave) massive Multiple-Input Multiple-Output (MIMO) systems in Internet of Vehicles (IoV) scenarios, the paper proposes a deep learning (DL) algorithm, Squeeze-and-Excitation Attention Residual Network (SEARNet), which integrates Squeeze-and-Excitation Attention (SEAttention) mechanism and residual module. Specifically, SEARNet considers the channel information as an image matrix, and embeds a SEAttention module in residual module to construct the SEAttention-Residual block. Through a data-driven approach, SEARNet can effectively extract key information from the channel matrix using the SEAttention mechanism, thereby reducing noise interference and estimating the channel in an accurate and efficient manner. The simulation results show that compared to two traditional and two DL channel estimation algorithms, the proposed SEARNet can achieve a maximum reduction in normalized mean square error (NMSE) of 97.66% and 84.49% at SNR of -10 dB, 78.18% at SNR of 5 dB, and 43.51% at SNR of 10 dB, respectively.

Data Augmentation for Bridging the Delay Gap in DL-Based Massive MIMO CSI Feedback

Data Augmentation for Bridging the Delay Gap in DL-Based Massive MIMO CSI Feedback

Deep Learning-Based CFO Estimation for Multi-User Massive MIMO With One-Bit ADCs

Deep Learning-Based CFO Estimation for Multi-User Massive MIMO With One-Bit ADCs

A Sectorized RS Reuse Massive MIMO for Massive IoT Networks

A Sectorized RS Reuse Massive MIMO for Massive IoT Networks

Max-Min SINR Analysis of STAR-RIS Assisted Massive MIMO Systems With Hardware Impairments

Max-Min SINR Analysis of STAR-RIS Assisted Massive MIMO Systems With Hardware Impairments

Corrections to “Fusing Channel and Sensor Measurements for Enhancing Predictive Beamforming in UAV-Assisted Massive MIMO Communications”

Corrections to “Fusing Channel and Sensor Measurements for Enhancing Predictive Beamforming in UAV-Assisted Massive MIMO Communications”

Over-The-Air Federated Learning Over Scalable Cell-Free Massive MIMO

Over-The-Air Federated Learning Over Scalable Cell-Free Massive MIMO

Near-Optimal Low-Complexity Hybrid Precoding for THz Massive MIMO Systems

Near-Optimal Low-Complexity Hybrid Precoding for THz Massive MIMO Systems

An Assessment of Receiver Algorithms for Distributed Massive MIMO Systems: Investigating Design Solutions and Performance

An Assessment of Receiver Algorithms for Distributed Massive MIMO Systems: Investigating Design Solutions and Performance

Energy efficient RF chains selection based on integrated Hungarian and genetic approaches for uplink cell‐free millimetre‐wave massive MIMO systems

Energy efficient RF chains selection based on integrated Hungarian and genetic approaches for uplink cell‐free millimetre‐wave massive MIMO systems

Optimizing secure power allocation in massive MIMO systems with an eavesdropper under imperfect CSI conditions

Optimizing secure power allocation in massive MIMO systems with an eavesdropper under imperfect CSI conditions

Joint beamforming and power splitting design for MISO downlink communication with SWIPT: a comparison between cell-free massive MIMO and small-cell deployments

Simultaneous wireless information and power transfer (SWIPT) has been advocated as a highly promising technology for enhancing the capabilities of 5G and 6G devices. However, the challenge of dealing with large propagation path loss poses a significant hurdle. To address this issue, massive multiple-input multiple-output (MIMO) is employed to enhance the efficiency of SWIPT in cellular-based networks with multiple small cells, and especially increase the energy for cell-edge users. In addition, by leveraging a large set of spatially distributed base stations to collaboratively serve SWIPT-enabled user equipment, the cell-free massive MIMO has the potential to provide even better performance than the conventional small-cell systems. In this work, we extend the investigation to include the application of SWIPT technology with alternating current (AC) logic in the cell-free networks and the small-cell networks and propose joint beamforming and power splitting optimization frameworks to maximize the system sum-rate, subject to the constraints on harvested energy, AC logic energy supply, and total transmit power. The optimization problem is shown to be non-convex, posing a significant challenge. To address this challenge, we resort to a two-stage decomposition approach. Specifically, we first introduce quadratic transform-based fractional programming (FP) algorithms to iteratively solve the non-convex optimization problems in the first stage, achieving near-optimal solutions with low time complexities. To further reduce the complexities, we also incorporate conventional schemes such as zero forcing, maximum ratio transmission, and signal-to-leakage-and-noise ratio for the design of beamforming vectors. Second, to determine the optimal power splitting ratio within the framework, we develop a one-dimensional (1-D) search algorithm to tackle the single variable optimization problem reduced in the second stage. These algorithms are then evaluated in the context of cell-free MIMO and small-cell networks with numerical experiments. The results show that the FP-based algorithms can consistently outperform those utilizing the conventional beamforming schemes, and the solutions of this work can achieve up to fivefold improvement in the system sum-rate than the small-cell counterpart while providing different but comparable performance trends in energy harvesting (EH).

Semisupervised Representation Contrastive Learning for Massive MIMO Fingerprint Positioning

Semisupervised Representation Contrastive Learning for Massive MIMO Fingerprint Positioning

Massive MIMO Precoding and Beamforming Techniques for Future Generation Communication System

Massive MIMO, which was initially suggested, is a critical technique for meeting the rapidly growing data demand of 5G networks. MIMO technology may provide the propagation channel with a greater degree of flexibility in terms of multiplexing gain or diversity gain. New disruptive technologies have been developed in response to the large increase in data traffic that the forthcoming 5G mobile communication system. This is a critical research area for 5G wireless communication since massive MIMO has the possible system to significantly boost throughput and data transmission charges, as well as residual energy and energy efficiency. As a result of three years of fast development in massive MIMO technology, spectrum efficiency has been enhanced to an unprecedented level by the combination of growing antennas and adopting an extremely duplex communication mechanism. It is achieved by modeling the interference signal first and then constructing an exactly opposite signal added to a pre-distorted signal to cancel it. Additionally, this article discusses precoding methods and energy efficiency.