Multiple-input Multiple-output Systems Research Articles

Machine learning-based intelligent localization technique for channel classification in massive MIMO

Multiple-input multiple-output (MIMO) technology has been widely adopted in wireless communications, which enables the simultaneous transmission of multiple data streams via multiple transmitting and receiving antennas. In a MIMO system with non-line-of-sight (NLOS), transmitted signals are reflected by various obstacles along the path, reaching the antenna at different angles and times. In 5G networks, the NLOS problem is a major challenge for massive MIMO localization, significantly reducing positioning accuracy. In this work, an intelligent localization technique based on NLOS identification and mitigation is proposed to address this problem. In this solution, a Convolutional Neural Network (CNN) based hybrid Archimedes-based Salp Swarm Algorithm (HASSA) technique is proposed to detect NLOS or the line of sight (LOS) and estimate the location. The accuracy can be analyzed by considering the angle of arrival of signals, threshold-based time of arrival, and time difference of arrival from different antennas. A novel reinforcement learning-based optimization approach is used for the mitigation of NLOS in the radio wave propagation path, which in turn reduces the computational complexity. We use the Ensemble Deep Deterministic Policy Gradient-Based Approach (EDDPG)-based Honey Badger algorithm (HBA) for the aforementioned process. The simulation of this approach assesses diverse scenarios and considers different parameters, and the approach is compared with various state-of-the-art works. From the simulation results, our proposed approach can be used for the identification and detection of LOS and NLOS components and can precisely enhance the localization compared with other approaches.

Research on parameter optimization method of thrust vector/aerodynamic rudder composite control law based on singular value method

Advanced aircraft with thrust vectoring/aerodynamic rudder layout have significant control coupling problems. The traditional single-loop control parameter design method cannot take into account the performance of multiple control loops in this scenario. Therefore, a control law parameter optimization method based on the singular value of the hysteresis matrix is proposed. First, the control parameter optimization interval is determined using the time domain control performance index. Then, the singular value method is used to measure the stability margin of the multiple-input multiple-output (MIMO) system on this basis, and the corresponding optimal objective function is established to optimize the controller parameters. The feasibility of this method is verified by numerical simulation. The results show that the designed control parameter optimization algorithm has better time domain control performance and larger system stability margin than the traditional single-loop control parameter design method.

Cell-Free Massive MIMO Power Consumption with Serial Front-Hauls

Massive MIMO deployments have been traditionally based on dedicated links in the front-haul, i.e., between the central processing units and the Access Points (APs). Recently, cell-free massive Multiple-Input Multiple-Output (MIMO) systems based on serial front-haul links have been discussed to simplify the deployments, among other reasons. However, the power consumption models currently used for cell-free massive MIMO deployments typically assume dedicated front-haul links. This paper highlights the inaccuracy of these models when applied to serial front-hauls and proposes simple adaptations to achieve more realistic results. The results obtained for an exemplary scenario show that the front-haul power would represent 61.73% of the total consumed power with the original models. In contrast, with the proposed adaptations, it could be as low as 1.59% of the total consumed power for some serial front-haul configurations. Additionally, the impact of considering APs with lower power consumption is studied, in which case, the percentages above would become 93.15% and 11.96%, respectively. Hence the importance of having power models that fit the front-haul topology.

SCANet: A lightweight deep learning network for massive MIMO CSI feedback based on spatial and channel attention mechanism

Multi-user massive multiple-input multiple-output (MIMO) communication systems consume too much downlink bandwidth due to the huge channel state information (CSI) feedback, deep learning-based CSI feedback approaches fortunately can alleviate the feedback overhead while obtaining an accurate CSI. However, there is a trade-off between the high feedback performance and low computational complexity. In this paper, a low-complexity CSI feedback approach is proposed based on spatial and channel attention mechanism, namely the Spatial and Channel Attention Network (SCANet). Specifically, the spatial and channel attention mechanism makes the network’s attention mainly focus on the specific spatial regions and key feature channels. We devise a serial architecture in the encoder that composes of Spatial and Channel Attention Block (SCAB) and Encoder Transformer Block. Moreover, we design a hybrid architecture in the decoder that composes of the CNNs Block and Decoder Transformer Block. These designs enable the network to effectively extract both global and local CSI features. Computer simulations in both the indoor and outdoor scenarios show that under the same system configurations, the proposed low-complexity SCANet achieves almost the same performance as the state-of-the-art network while reducing the computational complexity by 85.76% fewer floating-point operations per second (FLOPS) on average.

Deep-Unfolded Tikhonov-Regularized Conjugate Gradient Algorithm for MIMO Detection

In addressing the multifaceted problem of multiple-input multiple-output (MIMO) detection in wireless communication systems, this work highlights the pressing need for enhanced detection reliability under variable channel conditions and MIMO antenna configurations. We propose a novel method that sets a new standard for deep unfolding in MIMO detection by integrating the iterative conjugate gradient method with Tikhonov regularization, combining the adaptability of modern deep learning techniques with the robustness of classical regularization. Unlike conventional techniques, our strategy treats the Tikhonov regularization parameter, as well as the step size and search direction coefficients of the conjugate gradient (CG) method, as trainable parameters within the deep learning framework. This enables dynamic adjustments based on varying channel conditions and MIMO antenna configurations. Detection performance is significantly improved by the proposed approach across a range of MIMO configurations and channel conditions, consistently achieving lower bit error rate (BER) and normalized minimum mean square error (NMSE) compared to well-known techniques like DetNet and CG. The proposed method has superior performance over CG and other model-based methods, especially with a small number of iterations. Consequently, the simulation results demonstrate the flexibility of the proposed approach, making it a viable choice for MIMO systems with a range of antenna configurations, modulation orders, and different channel conditions.

Performance analysis of multiple-input multiple-output orthogonal frequency division multiplexing system using arithmetic optimization algorithm

This research aims to optimize the interference mitigation and improve system performance metrics, such as bit error rates, inter-carrier interference (ICI), and inter-symbol interference (ISI), by integrating the Redundant Discrete Wavelet Transform (RDWT) with the Arithmetic Optimization Algorithm (AOA). This will increase the spectral efficiency of MIMOOFDM systems for ultra-high data rate (UHDR) transmission in 5 G networks. The most important contribution of this study is the innovative combination of RDWT and AOA, which effectively addresses the down sampling issues in DWT-OFDM systems and significantly improves both error rates and data rates in high-speed wireless communication. Fifth-generation wireless networks require transmission at ultra-high data rates, which necessitates reducing ISI and ICI. Multiple-input multiple-output orthogonal frequency division multiplexing (MIMOOFDM) is employed to achieve the UHDR. The bandwidth and orthogonality of DWT-OFDM (discrete wavelet transform-based OFDM) are increased; however system performance is degraded due to down sampling. The redundant discrete wavelet transform (RDWT) is proposed for eliminating down sampling complexities. Simulation results demonstrate that RDWT effectively lowers bit error rates, ICI, and ISI by increasing the carrier-to-interference power ratio (CIR). The Arithmetic Optimization Algorithm is used to optimize ICI cancellation weights, further enhancing spectrum efficiency. The proposed method is executed in MATLAB and achieves notable performance gains: up to 82.95 % lower error rates and 39.88 % higher data rates compared to the existing methods. ConclusionThe integration of RDWT with AOA represents a significant advancement in enhancing the spectral efficiency of MIMOOFDM systems for UHDR transmission in 5 G networks. The proposed method not only enhances system performance but also lays a foundation for future developments in high-speed wireless communication by addressing down sampling issues and optimizing interference mitigation.

Design of a compact quad-port wideband printed MIMO antenna for vehicular communication environment

ABSTRACT A wideband quad-port Multiple Input Multiple Output (MIMO) antenna with efficient isolation characteristics for 5 G New Radio (5 G-NR) applications is presented. The proposed antenna works in the span of 3–6 GHz frequency covering the n77/n78/n79 bands also covering the 5 G-V2X band (3300–5000 MHz), LTE-46 band (5150–5925 MHz) and Dedicated Short Range Communication (DSRC) at 5.9 GHz band. The single element of the MIMO antenna structure is modelled by an elliptical structure stacked on a rectangular patch along with the feedline on the conducting plane. The ground plane is modelled as a rhombic design providing a Defected Ground Structure (DGS) to provide the resonance at the desired frequency band. Later, the antenna is evolved as a four-element MIMO system by arranging the antenna elements orthogonal to each other with a distance of separation between the adjacent antenna elements considered to be 10 mm. The all-inclusive dimension of the proposed antenna is 65 × 65 mm2. The simulated MIMO structure is fabricated and measured for antenna parameters and MIMO parameters such as isolation characteristics and MIMO diversity performances. The simulated values are obtained at acceptable limits with the measured values for the proposed wideband frequency range of operation. The compact, wideband antenna is found to be suitable for various 5 G-NR applications, especially Wi-Fi-assisted Vehicular Communication in the sub-6 GHz frequency range.

MIMO system identification and uncertainty calibration with a limited amount of data using transfer learning

Multiple-input multiple-output (MIMO) systems are fundamental in numerous advanced engineering applications, from aerospace to telecommunications, where precise system identification is critical for optimal performance. However, the identification of such systems often faces significant hurdles due to data scarcity, with existing approaches typically requiring substantial amounts of data for effective training. Addressing this challenge, this paper introduces a novel transfer learning framework designed specifically for MIMO system identification under conditions of limited data and inherent uncertainties. The proposed framework is applied to two case studies: the first in metal additive manufacturing, specifically the laser-blown powder-directed energy deposition as the source domain and the laser hot wire-directed energy deposition as the target domain, and the second involving a nonlinear case study of a continuous stirred-tank reactor (CSTR) with a temperature-dependent reaction. The results underscore the framework's effectiveness in capturing the dynamics of the target systems, including the ability to effectively model nonlinear dynamics. Comparative analyses highlight the benefits of employing dimensionless numbers in dynamic system modelling, offering reduced dimensionality, more physical meaning, and increased model accuracy. Overall, the proposed framework presents a promising approach to enhance system identification in MIMO systems with limited data and uncertainties, with potential applications across diverse domains.

Enhancing Wireless Communication Efficiency Using Dynamic Nonlinear Distortion Adaptive Optimization Analysis

Abstract Modern communication networks require improved signal quality and spectrum efficiency. Modern wireless communication relies heavily on the multiple input multiple output (MIMO) technology to improve spectrum efficiency and achieve faster data rates. However, the performance of MIMO systems is significantly hampered by non-linear distortions in power amplifiers (PA), especially under dynamic operating conditions. Traditional methods to reduce these distortions often lack the adaptability required for effective optimization. The proposed dynamic nonlinear distortion adaptive optimization analysis (DNDAOA) therefore introduces an adaptive approach that dynamically adjusts the pre-distortion signals using real-time feedback mechanisms. DNDAOA effectively reduces non-linear distortions and thus improves the performance of MIMO systems. Simulation results show that DNDAOA significantly increases spectrum efficiency, reduces error rates, and improves signal quality. This method is widely used in areas such as wireless telecommunications, internet of things (IoT), autonomous vehicles, and smart infrastructure, driving advances in connectivity, reliability, and data integrity.

Joint Power Allocation and Hybrid Beamforming for Cell-Free mmWave Multiple-Input Multiple-Output with Statistical Channel State Information.

Cell-free millimeter wave (mmWave) multiple-input multiple-output (MIMO) can effectively overcome the shadow fading effect and provide macro gain to boost the throughput of communication networks. Nevertheless, the majority of the existing studies have overlooked the user-centric characteristics and practical fronthaul capacity limitations. To solve these practical problems, we introduce a resource allocation scheme using statistical channel state information (CSI) for uplink user-centric cell-free mmWave MIMO system. The hybrid beamforming (HBF) architecture is deployed at each access point (AP), while the central processing unit (CPU) only combines the received signals by the large-scale fading decoding (LSFD) method. We further frame the issue of maximizing sum-rate subject to the fronthaul capacity constraint and minimum rate constraint. Based on the alternating optimization (AO) and fractional programming method, we present an algorithm aimed at optimizing the users' transmit power for the power allocation (PA) subproblem. Then, an algorithm relying on the majorization-minimization (MM) method is given for the HBF subproblem, which jointly optimizes the HBF and the LSFD coefficients.

A systematic review of design and performance enhancement techniques for the reduction of radar cross section in multiple input multiple output antennas

multiple-input multiple-output technology has emerged as a pivotal solution for addressing the capacity constraints of high-speed broadband wireless networks. By employing multiple antennas at both the transmitter and receiver, multiple-input multiple-output significantly enhances wireless communication efficiency. This paper presents a comprehensive review of multiple-input multiple-output antenna design strategies tailored for fifth-generation (5 G) networks and beyond. Specifically, it critically examines methodologies aimed at mitigating radar cross section in multiple-input multiple-output antennas, a crucial aspect of antenna engineering, especially in scenarios requiring reduced detectability. Various design and performance enhancement techniques proposed in the literature for RCS reduction in multiple-input multiple-output antennas are systematically analyzed, focusing on aspects such as antenna geometry and materials methods. Synthesizing these findings provides valuable insights into the current state of research, highlighting advancements, challenges, and future directions in developing efficient multiple-input multiple-output antennas with minimized radar cross section. This review contributes to antenna technology by aiding researchers and practitioners in informed decision-making regarding radar cross section reduction techniques for multiple-input multiple-output systems, and it identifies innovative approaches pertinent to multiple-input multiple-output applications. Finally, key considerations in multiple-input multiple-output antenna design are addressed, proposing potential directions for future research and development initiatives.

Block-term tensor decomposition in array signal processing

The ability of tensor decomposition (TD)-based methods to recover latent information, including channel parameters and transmitted symbols in the array signal processing (ASP) context, in a deterministic manner and with little or no training overhead, fits well with the data efficiency needs of future-generation multi-user massive multiple-input multiple-output systems. However, such methods have mostly relied on a few classical TD models, notably the canonical polyadic decomposition (CPD), which can be quite restrictive in realistic scenarios. In this paper, a generalized CPD model, known as block-term decomposition (BTD), is re-visited in the context of ASP, and shown to be the natural choice in sensor arrays of increased dimensionality that also involve channel multipath. Semi-blind joint channel estimation/data detection (JCD) is addressed in this context via efficient algorithms that wed existing JCD schemes with BTD approximation. Robust to sensor failures and recursive versions are also developed. The special yet important case of the uniform rectangular array (URA) configuration is adopted to illustrate the ideas and results. The signal detection performance of the BTD-inspired semi-blind JCD schemes is evaluated under various conditions with the aid of simulations and seen to be favorably compared with that of the training-only-based solution.

DGD-CNet: Denoising Gated Recurrent Unit with a Dropout-Based CSI Network for IRS-Aided Massive MIMO Systems.

For the deployment of Sixth Generation (6G) networks, integrating Massive Multiple-Input Multiple-Output (Massive MIMO) systems with Intelligent Reflecting Surfaces (IRS) is highly recommended due to its significant benefits in reducing communication losses for Non-Line-of-Sight (NLoS) conditions. However, the use of passive IRS presents challenges in channel estimation, mainly due to the significant feedback overhead required in Frequency Division Duplex (FDD)-based Massive MIMO systems. To address these challenges, this paper introduces a novel Denoising Gated Recurrent Unit with a Dropout-based Channel state information Network (DGD-CNet). The proposed DGD-CNet model is specifically designed for FDD-based IRS-aided Massive MIMO systems, aiming to reduce the feedback overhead while improving the channel estimation accuracy. By leveraging the Dropout (DO) technique with the Gated Recurrent Unit (GRU), the DGD-CNet model enhances the channel estimation accuracy and effectively captures both spatial structures and time correlation in time-varying channels. The results show that the proposed DGD-CNet model outperformed existing models in the literature, achieving at least a 26% improvement in Normalized Mean Square Error (NMSE), a 2% increase in correlation coefficient, and a 4% in system accuracy under Low-Compression Ratio (Low-CR) in indoor situations. Additionally, the proposed model demonstrates effectiveness across different CRs and in outdoor scenarios.

Enhanced channel estimation with atomic norm minimization and reconfigurable intelligent surfaces in mmWave MIMO systems

SummaryThe performance of millimeter‐wave (mmWave) multiple‐input multiple‐output (MIMO) systems has been significantly enhanced by the incorporation of dynamic reconfigurable intelligent surfaces (RIS). This paper proposes a novel dynamic channel estimation technique that combines dynamic atomic norm minimization with dynamic RIS to optimize RIS‐aided mmWave MIMO systems. Leveraging the dynamic nature of both atomic norm minimization and RIS, the proposed approach efficiently adapts to changing environmental conditions, providing robust and accurate channel estimation. By dynamically optimizing the RIS configuration, the system achieves improved spectral and energy efficiency, enabling high‐speed and reliable communication in challenging mmWave environments. Theoretical analysis and simulation results demonstrate the effectiveness of the proposed dynamic channel estimation technique, highlighting its potential for enhancing the performance of future wireless communication systems.

Dual-band millimetre wave MIMO antenna with reduced mutual coupling based on optimized parasitic structure and ground modification

In this study, a high-isolation dual-band (28/38 GHz) multiple-input–multiple-output (MIMO) antenna for 5G millimeter-wave indoor applications is presented. The antenna consists of two interconnected patches. The primary patch is connected to the inset feed, while the secondary patch is arc-shaped and positioned over the main patch, opposite to the feed. Both patches function in the lower 28 GHz band, while the primary patch is accountable for inducing the upper 38 GHz band. An expedited trust-region (TR) algorithm is employed to optimize the dimensions of the antenna components, ensuring the antenna operates efficiently with high reflection at both bands. The antenna demonstrates a gain exceeding 7 dBi at both frequencies. An array of four antennas is configured orthogonally to create a MIMO system with isolation surpassing 19 dB. The isolation is further enhanced through the addition of a circular parasitic patch at the front and modifications made to the ground. The TR method is employed again to optimize their parameters and achieve the desired isolation, exceeding 32 dB at both bands. The MIMO system demonstrates outstanding diversity performance at both frequencies, characterized by low values of the envelope correlation coefficient (ECC) (< 10 − 4), channel capacity loss (CCL) (< 0.03 bit/s/Hz), and total active reflection coefficient (TARC) (< − 10 dB). Additionally, it secures a diversity gain (DG) exceeding 9.99 dB. The MIMO system is manufactured and tested, showing good alignment between simulation and measurement data for all performance metrics.

Capacity Optimization for RSMA-Based Multi-User System over Underwater Turbulence Channel

The underwater environment used for communication is harsh and complex, necessitating heightened standards for spectral efficiency and reliability in underwater wireless optical communication (UWOC) systems. The focus of this work is on the performance of multi-user UWOC systems operating in oblique channels of ocean turbulence downlink, where users are randomly distributed at a certain depth. A joint optimization scheme is proposed, which joints rate-splitting multiple access (RSMA) and power allocation so that the system’s ergodic sum capacity is optimized to improve the transmission bandwidth. Furthermore, the probability density function (PDF) and cumulative distribution function (CDF) models for the received signal-to-noise ratio (SNR) of a multi-user multiple-input multiple-output (MIMO) system operating in the turbulent underwater oblique channels are established, accounting for the avalanche photodiode (APD) shot noise and solar radiation noise. Theoretical derivations are presented to quantify the ergodic capacity and outage probability of the multi-user system utilizing the RSMA technology. Subsequently, a numerical analysis is conducted to investigate the influence of the power allocation coefficient, RSMA, and the joint optimization algorithm on the performance of a two-user MIMO system leveraging RSMA. The simulation results show that our optimization scheme effectively reduces the outage probability, thereby achieving the maximum system sum rate and validating the practical feasibility and efficacy of the proposed scheme.

Distributed Joint Beamforming for RIS-assisted Cell-Free MIMO with Discrete Phase Shifts

Abstract Both reconfiurable intelligent surface (RIS) and cell-free multiple-input multiple-output (CF-MIMO) system are holding promise to become the predominant research direction in 6G networks. This paper studies the problem of weighted sum-rate maximization(WSR) in RIS-assisted CF-MIMO networks with discrete phase shifts. We utilize alternating optimization (AO) to iteratively calculate active and passive beamforming. At access points (APs) side, we employ a distributed algorithm to design the active beamforming, leveraging information interact among APs without centralized designing at central processing unit(CPU). Facing the constraint of discrete phase shift in RIS, we adopt semidefinite relaxation(SDR) projection and proximal gradient descent(PGD) to design the passive beamforming respectively. Simulation results indicate that our proposed algorithms achieve performance comparable to the distributed framework with continuous phase shift while reducing the computational time.

High isolation MIMO antenna array for multiband terahertz applications

The advancement of future technology seeks to create wireless systems capable of achieving faster data transfer rates. One solution to confront this challenge is by employing Multiple-Input Multiple-Output (MIMO) systems. Nonetheless, the primary obstacle in assessing MIMO performance revolves around the size and spacing of the antennas to maintain isolation among the ports. Consequently, it is essential to devise an approach to tackle these challenges, with the utilization of a DGS as a viable and promising approach. Hence, in this study, we developed a dual-frequency MIMO system featuring two linear array antennas with two elements each and configured to function at 128 GHz and 178 GHz. Initially, this design is conceived as a single-element structure before evolving into a two-element MIMO antenna array configuration. The antenna's physical dimensions are 2.7 × 3.65 × 0.15 mm3, and it consists of two linear array nodes positioned at a separation distance of 0.18 mm. Additionally, the antenna delivers peak gains of 5.16 dB and 6.24 dB at frequencies of 128 GHz and 178 GHz, respectively. The values of various MIMO performance indicators meet stringent standards with ECC values below 0.012 and a DG of approximately 10 dB. Furthermore, the TARC remains below 0 dB. The aim of this research is to create a novel design for a THz antenna that can achieve a wide bandwidth and a high gain while maintaining the standard compact antenna size necessary for THz applications.

A transparent experimental modelling method for linear multiple-input multiple-output systems

This paper presents a transparent technique that simulates processes using input and output measurement data without the use of any complex optimization or iterative algorithms. The method is very simple and easy to understand as it comprises only of linear ordinary differential equations, Laplace transformations and least squares technique. The solution can be expressed only in two linear matrix equations. The main aim is to provide bachelor students of engineering with a tool to formulate transfer functions.The method is validated with artificially generated erroneous measurement data as well as using measurements obtained from a practical application at the university. Inclusion of dead times and estimation of initial conditions make it ideal to be used for various range of applications such as stable and unstable systems with and without damping, open and closed loop systems. Over-integration, normalizing and/or zeroing of different coefficients add more degrees of freedom to improve the model quality.

In-Band Full-Duplex Multiple-Input Multiple-Output Systems for Simultaneous Communications and Sensing: Challenges, methods, and future perspectives [Special Issue on Signal Processing for the Integrated Sensing and Communications Revolution

In-Band Full-Duplex Multiple-Input Multiple-Output Systems for Simultaneous Communications and Sensing: Challenges, methods, and future perspectives [Special Issue on Signal Processing for the Integrated Sensing and Communications Revolution