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Multiple input-multiple output (MIMO) is a key enabling technology for the next generation of wireless communication systems. However, one of the main challenges in the implementation of MIMO system is the complexity of the detectors when the number of antennas increases. This aspect will be crucial in the implementation of future massive MIMO systems. A flexible design can offer a convenient tradeoff between detection complexity and bit error rate (BER). Deep learning (DL) has emerged as an efficient method for solving optimization problems in different areas. In MIMO communication systems, neural networks can provide efficient and innovative solutions. This paper presents an efficient DL-based signal detection strategy for MIMO communication systems. More specifically, a preprocessing stage is added to label the input signals. The labeling scheme provides more information about the transmitted symbols for better training. Based on this strategy, two novel schemes are proposed and evaluated considering BER performance and detection complexity. The performance of the proposed schemes is compared with the conventional one-hot (OH) scheme and the optimal maximum likelihood (ML) criterion. The results show that the proposed OH per antenna (OHA) and direct symbol encoding (DSE) schemes reach a classification performance F1-score of 0.97. Both schemes present a lower complexity compared with the conventional OH and the ML schemes, used as references. On the other hand, the OHA and DSE schemes have losses of less than 1 dB and 2 dB in BER performance, respectively, compared to the OH scheme. The proposed strategy can be applied to adaptive systems where computational resources are limited. [ABSTRACT FROM AUTHOR]

Multiple degree-of-freedom (DOF) mechatronic systems, such as robots and robotic arms, play a crucial role in modern life and production. However, due to strong coupling, uncertain dynamics, and external disturbance, accurately modeling these systems is challenging, making traditional model-based control methods impractical. To address this, this paper proposes an extremum-seeking-based adaptive enhanced model-free control for multi-input multi-output (MIMO) mechatronic systems to realize robust trajectory tracking. Unlike previous model-free control methods that decouple and reorganize the MIMO system into several single-input single-output ultra-local models, this paper develops a MIMO ultra-local model with a non-diagonal gain matrix α to approximate the system dynamics within an ultra-short time window. Time-delay estimation (TDE), Proportional–Derivative (PD) control law and accuracy compensation compose an TDE-based enhanced intelligent PD control that ensures the closed-loop stability. Furthermore, an extremum-seeking (ES) technique is designed to optimize the gain matrix α to enhance control performance. The main contributions of this paper are the development of a model-free control framework based on the MIMO ultra-local model and the successful application of ES to optimize the non-diagonal gain matrix α. Stability analysis of the closed-loop system is conducted using Lyapunov theorem. Finally, numerical simulations on a 2-DOF robotic manipulator and co-simulation results on a 3-DOF PUMA 560 robotic manipulator validate the effectiveness and superiority of the proposed methods. • A novel MIMO-based ULM with a non-diagonal matrix gain is proposed to achieve MFC. • A simple signum function is used to compensate for the estimation error of TDE. • An extremum-seeking strategy is used to optimize gain matrix for optimal performance. [ABSTRACT FROM AUTHOR]

Z-periodic complementary sequence sets (ZPCSSs) have potential applications in multi-carrier code-division multiple access (MC-CDMA) communication systems and MIMO channel estimation due to their favourable correlation properties. In this paper, we explore several methods to design optimal ZPCSSs. More specifically, combining complete complementary code (CCC) and optimal ZPCSS, we design optimal ZPCSSs with flexible parameters, using the Kronecker product. Besides, using product sequences, we obtain the optimal ZPCSS with a large zero correlation zone (ZCZ) and optimal ZPCSSs with flexible flock sizes, respectively. Finally, we can obtain more optimal ZPCSSs using the left shift operator. In particular, some lengths of the resultant sequence sets have not been reported before. [ABSTRACT FROM AUTHOR]

This article details a compact octa-port Multi-Input-Multi-Output (MIMO) antenna tailored for Intelligent Transportation System (ITS) applications. The process begins with crafting a shared radiator-based two-element array for dual-port MIMO functionality, employing orthogonal feeding to ensure high isolation – a critical factor for optimal performance. The apex of our design efforts results in an octa-port MIMO antenna, built upon the foundation element, the dual-port MIMO system with shared radiators. This system is meticulously engineered to attain maximum isolation between its ports. Under worst-case conditions during orthogonal mode excitation, isolation measures at −18 dB, while in other cases, isolation exceeds −20 dB. Impedance bandwidths of 5.5%, 4.13%, 5.1%, and 4% are attained for Port1, Port2, Port3, and Port4, respectively (only four ports are given due to symmetry). The article delves extensively into various MIMO performance metrics, encompassing the total active reflection coefficient (TARC), diversity gain (DG), channel capacity loss (CCL), and envelope correlation coefficient (ECC). Crucially, the measured performance consistently falls within acceptable thresholds, confirming the suitability of this specified MIMO antenna for intelligent automotive applications, especially within the realm of Intelligent Transportation Systems (ITS). [ABSTRACT FROM AUTHOR]

With the current advancements in millimeter wave communication, low‐profile structure and power handling capability at high frequencies have become the prime focus for the researchers. substrate integrated waveguide (SIWs), providing better efficiency by exhibiting high‐quality factors and confining EM waves, have become a favorable technology in the antenna regime. Recently, SIWs along with liquid metals have facilitated reconfigurability with extremely thin substrate materials. Antenna on‐chip (AOC) using Complementary Metal Oxide Semiconductor (CMOS) technologies along with Multiple Input and Multiple output (MIMO) applications are showing great potential for researchers in this domain. This review article intends to provide a comprehensive overview of the different types of SIW antennas operating in the 5G band, various SIW topologies, detailed analysis of gain, bandwidth, and isolation. Slotted SIW antennas give exceptional isolation between the bands and low cross‐polarization levels while horn antenna arrays offer wide coverage. Slow wave SIW has achieved an 80% reduction in size with the usage of multi‐antipodal metalized blinds via holes and distributed metal strips. Details on advancement in the artificial intelligence (AI) based SIW antennas are also presented in brief. [ABSTRACT FROM AUTHOR]

Summary: The 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. [ABSTRACT FROM AUTHOR]

The proposed work focuses on developing a novel low-profile decoupling network (DN) that utilizes near-field suppression mechanisms to enhance the isolation in a compact MIMO antenna system. Through an extensive analysis of near-field coupling, the operation mechanism of the DN is thoroughly investigated, forming the basis for its development. The authenticity of the DN is validated through design and testing across various frequencies, including 3G,4G, and 5G. The manuscript emphasizes the effectiveness of the DN specifically in the Sub-6 GHz band (3.5 GHz) for 5G applications. For this, a quad-element MIMO antenna system (36 × 36 mm2) that comprises an orthogonally closed spaced (≈ λ0/14) monopole antenna is designed. The integration of DN leads to a significant improvement in isolation, achieving (>15 dB) and (>35 dB) isolation between adjacent and diagonal elements at 3.5 GHz, respectively. The overall isolation is more than 25 dB throughout the operating band. Furthermore, the MIMO performance parameters are evaluated for the entire frequency band to ensure its applicability for early 5G applications. [ABSTRACT FROM AUTHOR]

Intelligent reflecting surface (IRS) holds tremendous potential as an efficient solution for achieving a dynamically reconfigurable wireless communication environment in a cost-efficient manner. This work explores the utilization of IRS to facilitate secure information transmission. We propose a double-IRS aided secure multiple-input multiple-output communication system model and derive the expression of the secrecy rate of this system. The introduction of eavesdropper and the coupled reflection matrices pose a significant challenge towards efficiently enhancing the secrecy rate through the joint optimization of the reflection matrices of IRSs and transmission gain matrix of the base station. To solve this problem, we propose the quantum dwarf mongoose optimization algorithm, which offers superior performance compared to the comparison algorithms. Furthermore, we compare the performance achieved by the single-IRS aided scheme with the double-IRS aided scheme and the scheme without IRS. Simulation results demonstrate that the incorporation of IRS results in a substantial enhancement of the secrecy rate due to the power amplification at the legitimate receiver and the interference suppression at the eavesdropper. [ABSTRACT FROM AUTHOR]

In the rapidly advancing field of edge computing, improving the end-to-end transmission rate is crucial to accommodating the needs of latency-sensitive applications. To address this, this article introduces Reconfigurable Intelligent Surfaces (RIS) to examine the challenge of maximizing the minimum attainable rate among users in a cell-free massive MIMO system from an edge computing perspective. In this article, a framework is proposed to improve the end-to-end user transmission rate by alternately optimizing the precoding matrix of Access Points (APs) and the phase shift matrix of the RIS. For the optimization of the APs' precoding matrix, this framework utilizes a Second Order Cone Programming (SOCP) method. In order to optimize the continuous phase shifts at the RIS, this framework uses a Semidefinite Relaxation (SDR) technique. For the optimization of the discrete phase shifts at the RIS, a projection-based method is proposed in this framework. By integrating these two forms of beamforming, the proposed framework significantly improves the end-to-end transmission rate, meeting the critical requirements of latency-sensitive applications in edge computing scenarios. [ABSTRACT FROM AUTHOR]

The number of bits required in phase shifters (PS) in hybrid precoding (HP) has a significant impact on sum-rate, spectral efficiency (SE), and energy efficiency (EE). The space and cost constraints of a realistic massive multiple-input multiple-output (MIMO) system limit the number of antennas at the base station (BS), limiting the throughput gain promised by theoretical analysis. This paper demonstrates the effectiveness of employing an intelligent reflecting surface (IRS) to enhance efficiency, reduce costs, and conserve energy. Particularly, an IRS consists of an extensive number of reflecting elements, wherein every individual element has a distinct phase shift. Adjusting each phase shift and then jointly optimizing the source precoder at BS and selecting the optimal phase-shift values at IRS will allow us to modify the direction of signal propagation. Additionally, we can improve sum-rate, EE, and SE performance. Furthermore, we proposed an energy-efficient HP at BS in which the analog component is implemented using a low-resolution PS rather than a high-resolution PS. Our analysis reveals that the performance gets better as the number of bits increases. We formulate the problem of jointly optimizing the source precoder at BS and the reflection coefficient at IRS to improve the system performance. However, because of the non-convexity and high complexity of the formulated problem. Inspired by the cross-entropy (CE) optimization technique used in machine learning, we proposed an adaptive cross-entropy (ACE) 1-3-bit PS-based optimization HP approach for this new architecture. Moreover, our analysis of energy consumption revealed that increasing the low-resolution bits can significantly reduce power consumption while also improving performance parameters such as SE, EE, and sum-rate. The simulation results are presented to validate the proposed algorithm, which highlights the IRS efficiency gains to boost sum-rate, SE, and EE compared to previously reported methods. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of designing a dynamic decoupler for a class of two-inputs two-outputs nonlinear MIMO systems with experimentally modeled dynamics. The work describes the well-known linear theory of dynamic decoupling of TITO plants and discusses problems related to its application to nonlinear systems. The solution of constructing a fuzzy dynamic decoupler with two possible approaches is proposed. The paper gives a practical example of the synthesis of such a system for the air heater, which is an example of nonlinear thermal plant. [ABSTRACT FROM AUTHOR]

In this paper, we propose a hybrid genetic algorithm (HGA) that embeds the tabu search mechanism into the genetic algorithm (GA) for multiple-input multiple-output (MIMO) detection. We modified the selection and crossover operation to maintain the diverse and wide exploration areas, which is an advantage of the GA, and the mutation operation to perform a local search for a specific region. In the mutation process, the 'tabu' concept is also employed to prevent the repeated search of the same area. In addition, a layered detection process is applied simultaneously with the proposed algorithm, which not only improves the bit error rate performance but also reduces the computational complexity. We apply the layered HGA (LHGA) to the MIMO system with very high modulation order such as 64-quadrature amplitude modulation (QAM), 256-QAM, and 1024-QAM. Simulation results show that the LHGA outperforms conventional detection approaches. Especially, in the 1024-QAM MIMO system, the LHGA has less than 10% of computational complexity but a 6 dB signal-to-noise ratio (SNR) gain compared to the conventional GA-based MIMO detection scheme. [ABSTRACT FROM AUTHOR]

This paper presents novel MIMO microstrip patch antennas with dimensions of 40 × 80 × 1.6 mm³ incorporating a decoupling and pattern correction structure (DPCS) designed to mitigate mutual coupling and radiation pattern distortion, operating within 3.6–3.7 GHz. Using characteristic mode analysis (CMA), two key modes affecting coupling and pattern degradation are identified, with the DPCS strategically positioned to address these issues. Unlike other decoupling techniques, the DPCS requires no additional space or structural complexity, making it suitable for 5G MIMO systems. The proposed design achieves isolation up to 90 dB and enhances the realized gain of Port 2 by 3 dB at boresight in simulations. Fabricated antennas were measured, achieving peak isolation of 80 dB in an anechoic chamber. Additionally, measurements in a noisy environment confirmed the robustness of the design under realistic conditions. Measured radiation patterns verified the DPCS's ability to correct the radiation pattern. Key MIMO performance metrics, including ECC (2 × 10⁻⁴), DG (≈ 10), CCL (< 0.2 bits/s/Hz), MEG (≈ -7 dB), and TARC (< -12 dB), affirmed the design's superior performance. The proposed structure can be applied to a variety of applications such as high-density urban wireless networks and IoT systems, where maintaining high isolation and reliable communication are critical requirements. [ABSTRACT FROM AUTHOR]

A multipurpose antenna system that can handle a broad area of frequencies is crucial in the effort to build up widespread 5G Internet-of-Things (IoT) networks. For fifth-generation Internet-of-things applications, this research introduces a new multi-band antenna that can operate in the sub-6 GHz band (2–7 GHz), Ku-band (13–17.5 GHz), and millimeter wave band (25–39 GHz). The antenna achieves a remarkable three-band operational bandwidth through cleverly integrated slots and parasitic components. Maximum realized gain of 4.3 dBi in the sub-6 GHz band, 5.5 dBi in the Ku-band, and 9.9 dBi in the millimeter wave (mm-wave) band for 3D MIMO setup is ensured. In addition, the machine learning prediction is used to verify the single element realized gain, and the results demonstrate that it performs admirably with an accuracy of more than 89% using the random forest regression model throughout the entire frequency spectrum. A 6-port, one-of-a-kind MIMO design with strong diversity performance is built from the single-element configuration. This 6-port MIMO system uses a new codesign technique to achieve 360-degree coverage in the elevation and azimuth planes, exceptional isolation (21 dB at sub-6 GHz band, 25 dB at the Ku-band, and 30 dB at mm-wave band), and pattern diversity. This MIMO antenna module is a shining example of the future, with the potential to completely alter the state of affairs in terms of 5G IoT connectivity in settings such as smart homes, offices, cities, vehicle-to-everything communications, and broadcast satellite service (BSS). [ABSTRACT FROM AUTHOR]

An overview of the architectures, functions and algorithms of aircraft-type aerospace vehicles equipped with fly-by-wire (FBW) digital integrated control systems (ICS) is given. The ICS has a hierarchical architecture, including the primary, backup and emergency control systems with a sophisticated control reconfiguration logic in the event of failures. Digital control computers have dissimilar redundancy of equipment and software. At the atmospheric flight stage, integrator control laws provide the specified control characteristics, flight envelope protection, automatic trim of vehicle and crew workload alleviation. Further integration of manual and automatic control, implementation of the control philosophy as a continuation of the pilot's actions, more intellectual control system and human-machine interface, use of multiple control surfaces to ensure optimal vehicle configuration and alleviate structural loading together with basic control functions are considered as promising areas for ICS improvement. • Controls allocation provides optimal aerodynamic configuration of aerospace vehicle. • Consideration of forces and moments as commands simplify control system synthesis. • Eigenvalues of MIMO system transfer functions matrix define its stability margins. • Load alleviation and control functions can be realized together in MIMO system. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to the development of a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing with index modulation (OFDM-IM) using the space-frequency block code (SFBC) technique. The system, which offers a high transmission data rate and high performance in fast-fading channel conditions even with an oversampled OFDM-IM signal and imperfect channel estimation, represents a significant advancement in the field. The main focus of this paper is to analyze the performance of this novel SFBC-based MIMO system with an oversampled OFDM-IM signal in terms of the biterror rate and throughput. Computer simulations unequivocally demonstrate that the performance of this system not only meets but surpasses that of conventional systems in highspeed wireless communications, marking a significant advancement in the field. The potential of the proposed method to inspire further research and development in the field is a key highlight, aiming to motivate the audience to explore new possibilities and contribute to the advancement of wireless communications. [ABSTRACT FROM AUTHOR]

The coupled relationship between inputs and outputs in multiple-input multiple-output (MIMO) systems, as well as the multiplicative uncertainties caused by multiplicative faults, increases the complexity of fault diagnosis (FD) and fault-tolerant control (FTC). Research has indicated that coprime factor uncertainties are suitable for modeling multiplicative uncertainties. This paper presents an FD and FTC strategy for MIMO systems based on the ν -gap metric technique within the coprime factorization framework. In the offline phase, the ν -gap metric-based hierarchical clustering method is designed to classify fault samples. Next, core systems and boundary systems are calculated for each fault category, and corresponding residual compensation controllers are designed. In the online phase, by computing the relevant ν -gap metric values, the fault severity of the real-time system is determined, and the core system with similar dynamic behaviors is identified. This FD result drives the switching of residual compensation controller, achieving FTC and ensuring system stability and robustness. This strategy eliminates the need for online solving of fault-tolerant controller, saving computational resources. Finally, the ν -gap metric-based FD and FTC strategy is validated with simulations on a three-phase voltage source inverter system. • Under the coprime factorization framework, FD and FTC strategy is presented. The Youla parameterization method is employed to parameterize the closed-loop system, resulting in a residual compensation controller. Fault tolerance control is realized by residual compensation controller. • By combining online and offline mechanisms, the results of FD directly drive the switching of fault-tolerant controllers, thereby reducing the online computational requirements and enhancing the timeliness of FD and FTC. • The v -gap metric-based hierarchical clustering algorithm is presented to identify faulty systems with similar dynamic performance. This approach effectively avoids redundancy in fault-tolerant controller design in situations with diverse faults. [ABSTRACT FROM AUTHOR]

Channel estimation poses a main challenge in intelligent reflecting surface (IRS)-assisted millimeter wave (mmWave) multi-user multiple-input multiple-output (MIMO) systems due to the substantial number of antennas at the base station (BS) and the passive reflective elements within the IRS lacking sufficient signal processing capabilities. This article addresses this challenge by proposing a channel estimation technique for IRS-assisted mmWave MIMO systems. The problem of channel estimation is normally taken as a compressed sensing (CS) problem, typically addressed through algorithms such as Orthogonal Matching Pursuit (OMP), Generalized Approximate Message Passing (GAMP), and Vector Approximate Message Passing with Expectation-Maximization (EM-VAMP). EM-VAMP demonstrates better performance only when a Gaussian mixture (GM) distribution is chosen as the prior for the sparse channel, especially at high signal-to-noise ratios (SNRs). To address this, the article introduces the application of generalized linear models (GLMs), extensions of standard linear models, providing increased flexibility in modeling data that deviates from Gaussian distribution. Numerical results unveil that the proposed Its EM-VAMP-GLM is much more robust to the existing OMP, GAMP and EM-LAMP algorithms. [ABSTRACT FROM AUTHOR]

Channel estimation poses critical challenges in millimeter-wave (mmWave) massive Multiple Input, Multiple Output (MIMO) communication models, particularly when dealing with a substantial number of antennas. Deep learning techniques have shown remarkable advancements in improving channel estimation accuracy and minimizing computational difficulty in 5G as well as the future generation of communications. The main intention of the suggested method is to use an optimal hybrid deep learning strategy to create a better channel estimation model. The proposed method, referred to as optimized D-LSTM, combines the power of a deep neural network (DNN) and long short-term memory (LSTM), and the optimization process involves the integration of the Reptile Search Algorithm (RSA) to enhance the performance of deep learning model. The suggested hybrid deep learning method considers the correlation between the measurement matrix and the signal vectors that were received as input to predict the amplitude of the beam space channel. The newly proposed estimation model demonstrates remarkable superiority over traditional models in both Normalized Mean-Squared Error (NMSE) reduction and enhanced spectral efficiency. The spectral efficiency of the designed RSA-D-LSTM is 68.62%, 62.26%, 30.3%, and 19.77% higher than DOA, DHOA, HHO, and RSA. Therefore, the suggested system provides better channel estimation to improve its efficiency. [ABSTRACT FROM AUTHOR]

This paper constructs a simulation platform for underwater wireless optical single-input single-output (SISO) communication systems and quantitatively evaluates communication performance indicators. To improve channel capacity, we propose an angular circle array MIMO scheme. The path loss, CIR, and channel capacity of the angular circular array MIMO communication system are calculated by using the Monte Carlo method. Results show that the proposed angular circular array MIMO communication system has a higher channel capacity compared to planar circular array MIMO communication systems and SISO communication systems. [ABSTRACT FROM AUTHOR]

Accurate channel estimation poses a significant challenge in the reconfigurable intelligent surface (RIS)-aided millimeter-wave (mmWave) wireless communication system. The fully passive nature of the RIS primarily relies on cascaded channel estimation, given its limitation in transmitting and receiving signals. Although the advantageous of this approach, the increase in the number of RIS elements leads to an exponential growth in the channel coefficient, resulting in costly pilot overhead. To address this challenge, the paper proposes a two-phase framework for separate channel estimation. The framework involves incorporating a few active elements within the passive RIS, enabling the reception and processing of pilot signals at the RIS. Through leveraging the difference in coherence time of the channel, the estimation of the time-varying channel among user equipment (UE) and RIS, as well as the estimation of the pseudo-static channel among RIS and base station (BS), can be performed separately. The two-phase separate channel estimation framework operates as follows: In the first phase, the BS-RIS channel is estimated at the RIS through the utilization of the few active elements. An iterative weighting methodology is employed to formulate the mathematical optimization problem for estimating the BS-RIS signal model. Subsequently, a proposed algorithm grounded on gradient descent (GD) is introduced to efficiently address and solve the optimization problem. In the second phase, the estimation of the UE-RIS channel is achieved by transforming the signal model of the received channel into an analogous tensor model known as Parallel Factor (PARAFAC). This transformation is followed by the application of the least squares (LS) algorithm within this tensor-based representation at BS. The effectiveness of the proposed framework is demonstrated through simulation findings, considering minimum pilot overhead, average spectral efficiency, and normalized mean square error (NMSE). A comparative analysis is performed with three other state-of-the-art existing schemes. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) empowered vehicular networks are capable of providing flexible radio access, efficient data transmission, and secure driving operation. For these promising applications, the authors focus on channel modelling for UAV‐to‐vehicle (U2V) communications in this paper, which is the very first step for communication system design. Based on geometry and multi‐dimensional mobility patterns, a geometry‐based stochastic model framework for U2V communication is studied. Considering the practical scenario, multipath components (MPCs) are composed of a line‐of‐sight (LoS) path, a ground reflection (GR) path, and several random scattering (SC) paths. Specifically, the movement of a UAV in both three‐dimensional (3D) translational and 3D rotational directions is considered as well as a ground vehicle capable of moving in arbitrary directions with varying velocities. Subsequently, the Doppler shift expressions for various paths and movements are presented individually, incorporating spatial angles. Comprehensive simulation results are examined, illustrating that six‐dimensional (6D) motion exhibits varying effects with linear relative velocity, local angular velocity, and two types of spatial angles. Moreover, channel statistical properties such as temporal auto‐correlation function (TCF) and Doppler power spectral density (PSD) are studied to show the consideration of 6D mobility is indispensable. [ABSTRACT FROM AUTHOR]

The accuracy analysis of dictionary sparse representation for channels in massive MIMO systems is a relatively unexplored field. Existing research has primarily focused on investigating the accuracy of dictionary sparse representation using simulation in massive MIMO systems, but has not provided quantitative accuracy analysis. To address this gap, the correlation numerical proportional factor is proposed to represent the accuracy performance of non‐zero elements in the coefficient matrix. Additionally, a qualitative analytical formula for dictionary sparse representation accuracy is provided and an optimal upper bound for the correlation numerical proportional factor is established. Furthermore, the innovation indicates that the accuracy of dictionary sparse representation is mainly influenced by the cross‐correlation between the pilots matrix and the dictionary matrix, as well as sparsity. The author has also developed a method for minimizing the correlation numerical proportional factor. In order to obtain an optimal sparse representation coefficient matrix, a cross‐correlation matrix is constructed and an analytical expression is derived for it as well as its use as an optimal hard decision threshold is determined. Finally, a sparse representation coefficient optimization algorithm is proposed using this optimal threshold. Simulation results demonstrate that this algorithm can significantly improve channel sparse dictionary representation accuracy. [ABSTRACT FROM AUTHOR]

Interference among wireless access points (APs) in Wi‐Fi systems limits the throughput of multi‐AP massive multiple‐input multiple‐output systems, and as the AP density increases, the increased interference leads to a significant loss of spectral efficiency of the system. Suppose interference is suppressed by obtaining information about all interfering channels, although the spectral efficiency of the system is greatly improved. In that case, the communication overhead between APs is too huge and consumes too many resources for coordinated transmission, and the performance improvement obtained is negligible. Based on this, a new deep learning hybrid precoding technique based on local channel information is proposed in this paper, where APs use local channel state information for direct hybrid precoding, which can effectively suppress inter‐AP interference in dense wireless local area network and improve the reachable rate of the system through the characteristics of deep learning networks. Through multi‐AP system‐level simulations, it is demonstrated that this non‐collaborative hybrid precoding method based on deep learning greatly suppresses interference and effectively improves the spectral efficiency of the system. [ABSTRACT FROM AUTHOR]

This study proposes a regression-based approach for calculating the likelihood probability in time-varying multi-input multi-output (MIMO) systems using one-bit analog-to-digital converters. These time-varying MIMO systems often face performance challenges because of the difficulty in tracking changes in the likelihood probability. To address this challenge, the proposed method leverages channel statistics and decoded outputs to refine the likelihood. An optimization problem is then formulated to minimize the mean-squared error between the true and refined likelihood probabilities. A linear regression approach is derived to solve this problem, and a regularization technique is applied to further optimize the calculation. The simulation results indicate that the proposed method improves reliability by effectively tracking temporal variations in the likelihood probability and outperforms conventional methods in terms of performance. [ABSTRACT FROM AUTHOR]

Summary: One of the advanced field in 5G cellular networks is the Massive Multiple‐Input‐Multiple‐Output (MIMO), which creates a massive antenna array by offering numerous antennas at the destination. This grows as a hot research topic in the wireless sectors as it enhances the volume and spectrum usage of the channel. The spectral efficiency (SE) is maximized using the abundant antennas employed by MIMO using spatial multiplexing of consumers, which needs precise channel state information (CSI). The SE is affected by both pilot overhead and pilot contamination. To mitigate the contamination and to estimate the suitable channel for communication, an efficient strategy is introduced using the proposed Namib Beetle Aquila optimization (NBAO)_Deep Q network (DQN). Here, the optimal pilot location is identified by employing NBAO, which is an integration of Namib beetle optimization (NBO) and Aquila optimizer (AO). Moreover, DQN is introduced to determine the suitable channel and metrics, such as bit error rate (BER) and normalized mean square error (MSE) is used for evaluation. The normalized MSE channel estimation is utilized to mitigate the effects of pilot contamination. Additionally, designed NBAO + DQN have attained a value of 0.0006 and 0.0005 for BER and normalized MSE. [ABSTRACT FROM AUTHOR]

Intelligent reflecting surfaces (IRSs) have emerged as a promising technology to enhance link reliability in a cost-effective manner, especially for line-of-sight (LOS) link blocking caused by obstacles. In this paper, we investigate an IRS-assisted single-cell photon-counting communication system in the presence of building shadows, where one IRS is deployed to assist the communication between a multi-antenna base station (BS) and multiple single-antenna users. Photon counting has been widely adopted in sixth-generation (6G) optical communications due to its exceptional detection capability for low-power optical signals. However, the correlation between signal and noise complicates analyses. To this end, we first derive the channel gain of the IRS-assisted MIMO system, followed by the derivation of the mean square error (MSE) of the system using probabilistic methods. Given the constraints of the transmit power and IRS configuration, we propose an optimization problem aimed at minimizing the MSE of the system. Next, we present an alternating optimization (AO) algorithm that transforms the original problem into two convex subproblems and analyze its convergence and complexity. Finally, numerical results demonstrate that the IRS-assisted scheme significantly reduces the MSE and bit error rate (BER) of the system, outperforming other baseline schemes. [ABSTRACT FROM AUTHOR]

In this work, a novel fractal multiple‐input multiple‐output (MIMO) antenna is designed for ultrawideband (UWB) applications. The radiating structure of the MIMO configuration employs a circular fractalradiating structure excited through a modified coplanar waveguide (CPW) feed. The fractal shape is designed by employing small circular rings inside the main circular ring patch element, which led to enhanced impedance bandwidth spanning from 3.15 to 20 GHz with acceptable radiation properties. The results show that the designed antenna has a peak gain of 6.13 dBi and radiation efficiency of > 85% in the operating bandwidth. Moreover, the single‐antenna element exhibits stable radiation patterns in the UWB frequency spectrum. For MIMO communication systems, a polarization diversity‐based quad‐port MIMO antenna is designed. From the designed MIMO antenna configuration, an isolation of > 15 dB is achieved among the radiating elements without the use of any isolation enhancement network. Furthermore, MIMO performance parameters such as envelope correlation coefficient (ECC) of < 0.008, mean effective gain (MEG) of < −3 dB, channel capacity loss (CCL) of < 0.25 bps/Hz, and total active reflection coefficient (TARC) of > 20 dB are obtained from the proposed UWB MIMO antenna. [ABSTRACT FROM AUTHOR]

In this article, we give some comments on the article 'A new time-varying feedback RISE control for second-order nonlinear MIMO systems: theory and experiments'. The article presents a new time-varying RISE control for second-order nonlinear MIMO systems and validates its ineffectiveness by experiments. We point out several flaws that occurred throughout the article, leading to the ineffectiveness of the proposed approach. A corrected control law is proposed and semiglobal asymptotic stability is proved. [ABSTRACT FROM AUTHOR]

The estimation of target directions of arrival (DOA) for wideband multiple‐input‐multiple‐output (MIMO) radar is investigated in this article. First, the authors establish a signal model for wideband MIMO radar systems. Then, an algorithm is proposed to estimate the target angles without secondary data (i.e. the training data from slow‐time is not required).The proposed algorithm unitises the spatial sparsity of target signals and it is derived under the Bayesian framework. It can estimate the spatial pseudo‐spectra of the targets through cyclic optimisation. Additionally, it is hyperparameter‐free and guarantees convergence. To analyse the performance of the proposed algorithm, the Cramér‐Rao bound (CRB) is derived for DOA estimation with wideband MIMO radar. Numerical examples demonstrate that even without secondary data, the proposed algorithm can accurately estimate the DOA of the targets. [ABSTRACT FROM AUTHOR]

The emergence of Beyond 5G (B5G) networks introduces novel challenges related to interference management, particularly within the context of Multiple-Input, Multiple-Output (MIMO) and Code Division Multiple Access (CDMA) technologies. In this comprehensive review paper, we delve into the intricacies of interference mitigation techniques within the B5G framework, with a specific focus on MIMO and CDMA systems. Firstly, we provide a brief overview of MIMO and CDMA principles, emphasizing their significance in B5G networks. MIMO leverages spatial diversity by employing multiple antennas in both the transmitter and the receiver, thereby enhancing capacity and reliability. CDMA, on the other hand, enables multiple users to share the same frequency band by assigning unique codes to each user. Next, we categorize the various types of interference encountered in MIMO and CDMA systems. These include co-channel interference, adjacent-channel interference, and multiuser interference. Understanding these interference sources is crucial for designing effective mitigation strategies. Our exploration of interference mitigation techniques covers state-of-the-art approaches tailored for MIMO and CDMA scenarios. Lastly, we discuss future research directions in interference mitigation for B5G networks. This review paper provides valuable insights for researchers, practitioners, and network designers seeking to enhance the robustness and efficiency of B5G communication systems by effectively mitigating interference in MIMO and CDMA contexts. [ABSTRACT FROM AUTHOR]

Pilot contamination has been identified as one of the major bottlenecks for the functioning of massive multiple-input-multiple-output (MIMO) cellular network. It is caused by the reuse of similar pilot signals by various users present in the cellular network required for channel estimation. In this paper, a cluster-based pilot reuse technique has been proposed where users present in different cluster reuse the same pilot signal. The technique is inspired from co-channel frequency reuse and allocates similar pilots to users which are at a considerable distance from each other. The simulation results presented substantiate the proficiency of the technique in eliminating the resisting effects of pilot contamination on system sum-rate in inter-cluster network. [ABSTRACT FROM AUTHOR]

This article presents an optimal pilot pattern for the data‐aided channel estimation (DACE) scheme for both single‐input single‐output (SISO) and multiple‐input multiple‐output orthogonal frequency division multiplexing (MIMO‐OFDM) wireless systems. The research evaluates the performance of the DACE scheme using different comb‐type pilot patterns for both least square (LS) and linear minimum mean square error (LMMSE) channel estimators. In this regard, it is found that pilot spacing significantly influences system performance. Inserting pilot symbols in consecutive subcarriers cannot compensate for increased pilot spacing. Hence, the solution to this problem is to place pilot symbols at appropriate locations within the given spectrum. Moreover, data symbols which are reliably detected at the receiver are used as additional pilot signals to further enhance system performance. However, reliable data symbols need to be determined carefully because wrong detection results in severe performance degradation. In this respect, the proposed comb‐type pilot pattern using a single pilot subcarrier extracts the maximum number of reliable data symbols for the DACE scheme, improves channel estimation accuracy, and provides bandwidth optimization for MIMO‐OFDM systems. Furthermore, it outperforms all other pilot patterns in terms of system mean square error (MSE) and bit‐error‐rate (BER) performance. [ABSTRACT FROM AUTHOR]

A continuous goal in all communication systems is to enhance users' experience and provide them with the highest possible data rates. Recently, the concept of cell-free massive MIMO (CF-mMIMO) systems has been considered to enhance the performance of systems that operate solely with radio-frequency (RF) or visible light communication (VLC) technologies. In this paper, a hybrid VLC/RF cell-free massive MIMO system is proposed where an RF cell-free network and a VLC cell-free network coexist to serve the users. The idea is to utilize the benefits of each network and balance the load with the aim of maximizing the system's sum-rate. The system is evaluated using zero-forcing (ZF) precoding scheme. Two distinct user association algorithms are proposed for assigning users to either the VLC network or the RF network. In addition, two user-centric clustering approaches are proposed and evaluated. Simulation results show that the proposed association algorithms significantly outperform a random network association of users in terms of sum-rate. Results also show great potential for the proposed system compared to standalone cell-free networks. [ABSTRACT FROM AUTHOR]

This study introduces a novel control approach for a twin rotor multi-input multi-output system (TRMS), specifically targeting the pitch and yaw movements. The proposed method employs a sliding mode controller (SMC) with variable gains, aiming to overcome limitations like chattering and excessive control effort. Unlike traditional controllers, the gains here dynamically adjust based on the error state, enhancing the robustness of the system against external disturbances and parameter uncertainties. The control algorithm draws inspiration from both first-order and higher-order sliding mode controllers. To rigorously assess the robustness of the controller, Gaussian White noise is incorporated into the model. By dynamically adjusting gains, the proposed approach aims to minimize control effort while mitigating control signal overestimation arising from model uncertainties. Furthermore, stability analysis confirms that the operating point of the closed-loop system converges within a finite time. The effectiveness of this novel controller is validated through simulations using the MATLAB/Simulink environment. [ABSTRACT FROM AUTHOR]

Stochastic geometry has emerged as a powerful tool for modeling cellular networks, especially in dense deployment scenarios where inter-cell interference is significant. Previous studies have extensively analyzed multi-antenna systems with partial channel state information at the transmitter (CSIT) using stochastic geometry models. However, most of these works assume the use of infinite-resolution analog-to-digital converters (ADCs) at the receivers. Recent advances in low-resolution ADCs, such as one-bit ADCs, offer an energy-efficient alternative for millimeter-wave systems, but the interplay between limited feedback and one-bit ADCs remains underexplored in such networks. This paper addresses this gap by analyzing the optimal feedback rate that maximizes net spectral efficiency in dense cellular networks, modeled using stochastic geometry, with both limited feedback and one-bit ADC receivers. We introduce an approximation of the achievable spectral efficiency to derive a differentiable expression of the optimal feedback rate. The results show that while the scaling behavior of the optimal feedback rate with respect to the channel coherence time remains unaffected by the ADC's resolution, the absolute values are significantly lower for one-bit ADCs compared to infinite-resolution ADCs. Simulation results confirm the accuracy of our theoretical approximations and demonstrate the impact of ADC resolution on feedback rate optimization. [ABSTRACT FROM AUTHOR]

In this paper, we propose a hybrid relay-reflecting intelligent surface (HR-RIS)-assisted cell-free (CF) massive multiple-input multiple-output (mMIMO) network to achieve consistent spectral efficiency (SE). The HR-RIS technique manipulates the propagation environment by reflecting and enhancing radio signals in desired locations, providing a symbiotic integration with CF mMIMO for future wireless communication systems. We first model uplink and downlink channels, obtaining minimum-mean-square-error estimations for efficient transmission paths. We then analyze the SE performance of the proposed system. To enhance sum spectral efficiency in downlink multi-antenna, multi-user, and millimeter-wave massive MIMO networks, we introduce a low-complexity hybrid precoding approach. The optimal analog equalizer is determined by converting the analog precoding matrix dimensions into square matrices and selecting a few discrete Fourier transforms to maximize the amplitude of corresponding wideband channel matrices. We employ the equal gain transmission technique to combine channel gains efficiently and ensure spectral efficiency. To mitigate inter-user interference, we propose an enhanced block diagonalization method for designing the digital precoder and combiner. Our study demonstrates that the proposed HR-RIS-assisted CF mMIMO system offers significant improvements in SE performance, paving the way for advanced wireless communication systems. [ABSTRACT FROM AUTHOR]

The utilization of fiber systems for transmitting millimeter-wave (MMW) signals has gained significant traction in recent years, particularly for advanced wireless communication applications such as 5G and beyond. This paper explores the integration of wireless and optical networks to enhance performance by reducing the error vector magnitude (EVM) and symbol error rate (SER). The proposed system employs a 2×2 multiple-input multiple-output (MIMO) configuration, which improves coverage and increases capacity through spatial multiplexing. MIMO systems are critical to modern wireless networks, providing superior spectrum and energy efficiency compared to earlier single-input-single-output systems. Following MIMO processing, a millimeter-wave signal is modulated onto the subcarrier using a Mach-Zehnder modulator (MZM). The signal is sent across a (50 and 70)-kilometer optical cable, which boosts data rate and frequency but introduces errors. The proposed method uses a convolutional neural network (CNN) correction to lower these errors and equalize to balance the SER and EVM. VPIphotonics and Python programming are utilized to put the system into practice. The proposed system has a bandwidth of 17 GHz and a data rate of 56.656 Gb/s; the center frequency is 160 GHz with EVM ≈ 3% at the 50 km distance and ≈ 4% at the 70 km fiber channel length. [ABSTRACT FROM AUTHOR]

Massive MIMO (Multiple-input-multiple output), crucial for 5 G and Beyond 5 G (B5G) networks, faces challenges with terahertz frequencies in B5G as the communication shifts from far-field to near-field. This shift disrupts traditional Massive MIMO channel estimation, leading to increased pilot overhead and limited performance. The current study used the hybrid-field orthogonal matching pursuit (HF-OMP) algorithm from the compressive sensing (CS) framework to estimate the channel with scatters from both far-field and near-field. The method, however, needs more flexibility regarding scatter location in the field of interest. Also, the usage of HF-OMP under a single measurement vector (SMV) scheme limits the overall performance of the existing method. To address these, we have proposed an adaptive hybrid-field simultaneous-OMP algorithm under multiple measurement vector (MMV) of the CS framework which selects multiple atoms having common support within a single iteration. This innovative approach tackles channel estimation for both far-field and near-field scatters while adapting to their varying distributions. Compared to classical methods, simulations show the new algorithm achieves up to a 14 % improvement in normalized mean square error within a 0 dB to 10 dB signal-to-noise ratio range. This translates to significant reductions in pilot overhead and enhanced channel reliability, paving the way for more efficient and robust wireless networks in the future. [ABSTRACT FROM AUTHOR]

Control decoupling is the basic control step for precision MIMO (Multi-Input-Multi-Output) motion systems. After decoupling, the MIMO logical controlled plant can be diagonal dominant so that the control design for each DOF (Degree of Freedom) can be separately treated. However, due to the manufacturing tolerances and the assembling errors, the actual CoG (Center of Gravity) and actuator positions are inconsistent with the designed values. The nominal static control decoupling matrix is inaccurate, which significantly deteriorates the decoupling performance. To address the problem, this paper develops a matrix update based iterative control decoupling tuning method. Tuning with feedback control signals to achieve accurate tuning is its first distinct feature. By employing rigid-body model information to construct more accurate basis functions, fast tuning can also be achieved, especially for relatively low control bandwidth occasions. Differing from the feedforward compensation based iterative control decoupling tuning method, the matrix update method improves the dynamics of the logical controlled plant, does not increase the control complexity and is more robust to the inaccuracy of the model information. Experimental results on the short-stroke module of a precision motion stage which is the key subsystem of the lithographic projection lens testing system present significant control decoupling performance improvement (for example the peak value of the Rx-DOF servo error caused by the Z-DOF movement is reduced from 1.29 ×10−5 m to 3.19 ×10−6 m) after just two trials. • This paper reveals the coupling mechanism of the multi-DOF motion stage in presence of the CoG and actuator position errors. • This paper analyzes the tuning principle by using feedback control signals to achieve accurate control decoupling tuning. • This paper proposes the matrix update based iterative control decoupling tuning method for the first time. [ABSTRACT FROM AUTHOR]

The paper aims to analyze the performance of wireless communication systems by focusing on key electrical parameters such as signal power, bandwidth, signal-to-noise ratio (SNR), attenuation, and modulation techniques. The study utilizes advanced concepts including MIMO (Multiple Input Multiple Output) systems, dynamic power control, and Shannon-Hartley theorem, all analyzed using MATLAB/Octave for simulation and optimization purposes. The investigation is conducted to optimize the efficiency and reliability of wireless networks, ensuring better resource management and improved quality of service (QoS). [ABSTRACT FROM AUTHOR]

Uplink massive Multiple-Input Multiple-Output (MIMO) systems include huge antennas in the base station (BS) that simultaneously serve fewer Users with single-antenna, making signal detection a major issue Due to the huge matrix inversion requirement. We propose an iterative detection technique based on the enhanced Alternating Direction Method of Multipliers-Conjugate Gradients (ADMM-CG) to avoid direct matrix inversion. The ADMM is first applied as an initial vector. Then, the CG iteration algorithm terminates the calculations for the rest of the iterations. A low-complexity initial method based on trace tridiagonal has been proposed to improve the suggested technique's performance. This integration is crucial for optimizing the tradeoff between performance and complexity. The proposed technique outperforms traditional iterative approaches regarding signal detection performance. Monte Carlo simulations show that the proposed detector performs near optimally, decreases complexity, and necessitates fewer iterations. Furthermore, it outperforms existing solutions, which are sensitive at high modulation orders and when the number of users approaches the entire number of antennas in the base station. [ABSTRACT FROM AUTHOR]

Hybrid analog and digital beamforming is gaining attention for its practical application in large-scale antenna systems. It offers significant cost savings, reduced complexity, and lower power consumption compared to entirely digital beamforming, all while maintaining comparable performance. This article proposes a hybrid beamforming architecture aimed at addressing these challenges by using a reduced number of radio frequency (RF) chains while achieving performance comparable to entirely digital schemes. The study demonstrates that matching the number of RF chains to the total number of data streams enables hybrid beamforming to compete effectively with entirely digital beamformers. The adopted approach focuses on computing analog and digital precoders and combiners using the metaheuristic method of genetic algorithms, in a point-to-point multiple input multiple output (MIMO) system scenario. The objective is to simplify the system and reduce costs by optimizing the number of antennas, RF chains, and data streams, all while maintaining comparable performance to entirely digital beamforming. The study's results show that increasing the number of antennas significantly impacts the quality and capacity of the hybrid massive MIMO beamforming system. Conversely, reducing the number of RF chains has a negligible effect on quality and capacity, but simplifies the design and minimizes costs. [ABSTRACT FROM AUTHOR]

In this paper, the performance of three model-free control approaches on a multi-input, multi-output (MIMO) nonlinear system with constant and time-varying references is compared. The first control algorithm is model-free adaptive control (MFAC). The second is a modified version of MFAC (MMFAC) designed to handle delays in the system by incorporating the output error difference (over two sample time steps) in the control input. The third approach, model-free adaptive predictive control (MFAPC) with a one-step-ahead forecast of the system input, is obtained by using predictions of the outputs based on the data-based linear model. The experimental device used is an MIMO three-tank system (3TS) assumed to be an interconnected system with multiple coupled single-input, single-output (SISO) subsystems with unmeasurable couplings. The novelty of this contribution is that each coupled SISO partition is assumed to be controlled independently using a decoupled control algorithm, leading to fewer control parameters compared to a centralized MIMO controller. Additionally, both parameter tuning for each controller and performance evaluation are conducted using an evaluation criterion considering energy consumption and accumulated tracking error. The results demonstrate that almost all the proposed model-free controllers effectively control an MIMO system by controlling its SISO subsystems individually. Moreover, the predictive features in the decoupled MFAPC contribute to more accurate tracking of time-varying references. The utilization of tracking error differences helps in reducing energy consumption. [ABSTRACT FROM AUTHOR]

In this paper, we propose an underlay cognitive radio (CR) system that includes subscribers, termed secondary users (SUs), which are designed to coexist with the spectrum owners, termed primary users (PUs). The suggested network includes the PUs system and the SUs system. The coexistence between them is achieved by using a novel dynamic spectrum co-access multicarrier-based cognitive radio (DSCA-MC-CR) technique. The proposal uses a quadrature phase shift keying (QPSK) modulation technique within the orthogonal frequency-division multiplexing (OFDM) scheme that maximizes the system data rate and prevents data inter-symbol interference (ISI). The proposed CR transmitter station (TX) and the CR receiver node (RX) can use an advanced smart antenna system, i.e., a multiple-input and multiple-output (MIMO) system that provides high immunity against channel impairments and provides a high data rate through its different combining techniques. The proposed CR system is applicable to coexist within different existing communication applications like fifth-generation (5G) applications, emergence applications like the Internet of Things (IoT), narrow-band (NB) applications, and wide-band (WB) applications. The coexistence between the PUs system and the SUs system is based on using power donation from the SUs system to improve the quality of the PU signal-to-interference-and-noise ratios (SINRs). The green communication concept achieved in this proposal is compared with similar DSCA proposals from the literature. The simulations of the proposed technique show enhancement in the PUs system throughput and data rate along with the better performance of the SUs system. [ABSTRACT FROM AUTHOR]