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This survey paper explores advanced nonlinear control strategies for Unmanned Aerial Vehicles (UAVs), including systems such as the Twin Rotor MIMO system (TRMS) and quadrotors. UAVs, with their high nonlinearity and significant coupling effects, serve as crucial benchmarks for testing control algorithms. Integration of sophisticated sensors enhances UAV versatility, making traditional linear control techniques less effective. Advanced nonlinear strategies, including sensor-based adaptive controls and AI, are increasingly essential. Recent years have seen the development of diverse sliding surface-based, sensor-driven, and hybrid control strategies for UAVs, offering superior performance over linear methods. This paper reviews the significance of these strategies, emphasizing their role in addressing UAV complexities and outlining future research directions. [ABSTRACT FROM AUTHOR]

Summary: An evaluation of various transmit precoding schemes is proposed in this thesis on basis of spectral efficiency (SE) of massive MIMO (m‐MIMO) system in 2 GHz spectrum. The present generation of wireless networks must be rethought due to the rapidly rising user base, huge volume data transfer, wide coverage requirement, and exponential rise in data rate. 5G mobile network includes a multi‐fold increase in network capacity, energy conservation, decreased latency, higher coverage probability, and a seamless connection. m‐MIMO has provided good solution for enhancing the data rate with the coverage, overcoming the effects of multipath fading and has also increased diversity of the system. Massiveness of MIMO system along with 5G network is the key technology to address the issue of SE, throughput, improvement of QoS (quality of service), coverage probability, power delay profile, diversity simulation, energy efficiency, and improvement in bit error rate. Parameters such as SE, throughput, and coverage probability are important for any communication system, and their improvement can increase overall capacity of cellular network system and could meet the requirement of upcoming demand for mobile network. The research paper is focused on performance analysis of m‐MIMO with reference to the SE of various transmit precoding schemes. Simulation results show that SE provided by M‐MMSE (multi‐cell minimum mean‐squared error) technique of precoding ranks highest for the same number of antennas and pilot reuse factor as compared to other schemes. [ABSTRACT FROM AUTHOR]

This paper discusses the operation of a massive multiple‐input multiple‐output (MIMO) system from a different perspective. The system is performed in the presence of an eavesdropper who is trying to disrupt the message being transmitted between the base station (BS) and the users. An attempt has been made to maximize the secure energy efficiency (EE) of the system by allocating appropriate power to each user. The channel state information (CSI) between users and the BS is considered imperfect and the employed precoding scheme is the zero forcing (ZF). The CSI related to the eavesdropper is considered to be perfect and its precoding is assumed to be the maximum ratio transmission (MRT). The problem of optimization that is for maximizing the EE has two constraints including the maximum transmission power and the minimum user data rate. To improve the performance of system, the cell division technique is applied, which results in approaching the performance of system to the without eavesdropper scenario. Five various scenarios have been investigated in this research, and it is proved that the proposed method improves the system performance in all cases. The numerical and simulation results obtained from implementation of the proposed algorithm confirm the claim. [ABSTRACT FROM AUTHOR]

Millimeter wave MIMO wireless communication systems are deployed in 5G and next‐generation networks. The effectiveness of deep learning models for improving the performance of these systems has been proven in the literature. However, several deep learning models are vulnerable to security threats, such as adversarial attacks. Therefore, for the deployment of these systems, it is essential to make them resilient to such kinds of attacks for good quality secure communication. Adversarial training is a solution by which deep learning models are trained for adversarial attacks beforehand. Adversarial training for three types of adversarial attacks, that is, Fast Gradient Sign Method, Iterative Fast Gradient Sign Method, and Momentum Iterative Fast Gradient Sign Method is implemented in this paper. The simulation results depict a decrease in the error at the receiving end after adversarial training, even after an adversarial attack has been applied. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

In this paper, the problem of joint transmission and computation resource allocation for a multi-user probabilistic semantic communication (PSC) network is investigated. In the considered model, users employ semantic information extraction techniques to compress their large-sized data before transmitting them to a multi-antenna base station (BS). Our model represents large-sized data through substantial knowledge graphs, utilizing shared probability graphs between the users and the BS for efficient semantic compression. The resource allocation problem is formulated as an optimization problem with the objective of maximizing the sum of the equivalent rate of all users, considering the total power budget and semantic resource limit constraints. The computation load considered in the PSC network is formulated as a non-smooth piecewise function with respect to the semantic compression ratio. To tackle this non-convex non-smooth optimization challenge, a three-stage algorithm is proposed, where the solutions for the received beamforming matrix of the BS, the transmit power of each user, and the semantic compression ratio of each user are obtained stage by stage. The numerical results validate the effectiveness of our proposed scheme. [ABSTRACT FROM AUTHOR]

A printed monopole super wideband (SWB) antenna is presented in this paper. It consists of a beveled-shaped radiator and a tapered ground plane. It is investigated that tapering of the radiator and ground plane enhances the impedance matching and results in broad impedance bandwidth. The experimental result depicts that the antenna achieved 164.9% impedance bandwidth from 2.6 to 27.27 GHz. Furthermore, to prevent the interference caused by communication systems within the ultra-wideband (UWB) range, dual-band rejection at Wi-MAX (2.7–4 GHz). and X-band (8.5–12 GHz) are introduced by etching an H-shape and two back-to-back C-shaped notches in the radiator and feedline, respectively. The measured result demonstrates that both slots effectively reject the desired frequency bands with a small shift in the lower frequency edge from 2.41 to 2.6 GHz. In addition, the proposed antenna application is shown in the two elements MIMO system where a bowtie decoupling structure is utilized to improve the isolation. It is found that the presence of a decoupling structure improves the isolation by up to 60%. To characterize the diversity performance, parameters such as Diversity Gain (DG), Envelop Correlation Coefficient (ECC), Channel Capacity Loss (CCL), and Mean Effective Gain (MEG) are calculated and all are found in the acceptable limit. [ABSTRACT FROM AUTHOR]

This paper considers a reconfigurable intelligent surface (RIS) aided multi-user multiple-input single-output (MU-MISO) mmWave downlink communication system. The hybrid beamforming (HBF) and the programmable reflecting elements (PREs) are respectively applied at the base station (BS) and the RIS, where the HBF consists of the digital beamforming (DBF) and the analog beamforming (ABF). To maximize the geometric mean of the users' rates (GM-rate) for providing feasible links to all users in the aspect of the same time slot and same bandwidth, which results in the superiority of a rational rate distribution for the users without imposing a minimum quality of service (QoS) constraint. The joint PREs, ABF, and DBF optimization problems are formulated, the problem is generally non-convex due to the log-determinant as well as unit modulus constraints for both PREs and ABF. Efficient alternating descent iteration algorithms are developed to solve the intractable problem. Finally, simulation results are included to verify the efficiency of the proposed approaches, results also show that the proposed algorithms can improve the fairness among users and accommodate discrete phase shifts for both PREs and ABF. [ABSTRACT FROM AUTHOR]

This paper presents a new technique for estimating the two-dimensional direction of departure (2D-DOD) and direction of arrival (2D-DOA) in bistatic uniform planar array Multiple-Input Multiple-Output (MIMO) radar systems. The method is based on the reduced-dimension (RD) MUSIC algorithm, aiming to achieve improved precision and computational efficiency. Primarily, this pioneering approach efficiently transforms the four-dimensional (4D) estimation problem into two-dimensional (2D) searches, thus reducing the computational complexity typically associated with conventional MUSIC algorithms. Then, exploits the spatial diversity of array response vectors to construct a 4D spatial spectrum function, which is crucial in resolving the complex angular parameters of multiple simultaneous targets. Finally, the objective is to simplify the spatial spectrum to a 2D search within a 4D measurement space to achieve an optimal balance between efficiency and accuracy. Simulation results validate the effectiveness of our proposed algorithm compared to several existing approaches, demonstrating its robustness in accurately estimating 2D-DOD and 2D-DOA across various scenarios. The proposed technique shows significant computational savings and high-resolution estimations and maintains high precision, setting a new benchmark for future explorations in the field. [ABSTRACT FROM AUTHOR]

In this paper, a predictive-based adaptive anti-saturation tracking control scheme is designed for uncertain multiple-input and multiple-output (MIMO) nonlinear systems subject to input constraints and external disturbance. Taking the safety of the MIMO nonlinear systems into account, a safety saturation threshold, which is less than the physical device saturation threshold, is given. In addition, since all signals of the auxiliary systems are not immediately employed to the systems when the input saturation happens, a new predictive-based auxiliary system is designed. Furthermore, to estimate the compound disturbance, with the help of the fuzzy control technology, the nonlinear disturbance observer (NDO) is used. Then, the stability of the closed-loop systems is proved under the designed controller. Finally, a simulation example is given to illustrate the feasibility of the designed anti-saturation controller. [ABSTRACT FROM AUTHOR]

The paper studies the multi-user precoding problem as a non-convex optimization problem for wireless multiple inputs and multiple outputs (MIMO) systems. In our work, we approximate the target Spectral Efficiency function with a novel computationally simpler function. Then, we reduce the precoding problem to an unconstrained optimization task using a special differential projection method and solve it by the Quasi-Newton L-BFGS iterative procedure to achieve gains in capacity. We are testing the proposed approach in several scenarios generated using Quadriga-open-source software for generating realistic radio channel impulse response. Our method shows monotonic improvement over heuristic methods with reasonable computation time. The proposed L-BFGS optimization scheme is novel in this area and shows a significant advantage over the standard approaches. The proposed method has simple implementation and can be a good reference for other heuristic algorithms in this field. [ABSTRACT FROM AUTHOR]

Reliable identification of spatial parameters for multiple-input multiple-output (MIMO) systems, such as the number of transmit antennas (NTA) and the direction of arrival (DOA), is a prerequisite for MIMO signal separation and detection. Most existing parameter estimation methods for MIMO systems only consider a single parameter in Gaussian noise. This paper develops a reliable identification scheme based on generalized multi-antenna time-frequency distribution (GMTFD) for MIMO systems with non-Gaussian interference and Gaussian noise. First, a new generalized correlation matrix is introduced to construct a generalized MTFD matrix. Then, the covariance matrix based on time-frequency distribution (CM-TF) is characterized by using the diagonal entries from the auto-source signal components and the non-diagonal entries from the cross-source signal components in the generalized MTFD matrix. Finally, by making use of the CM-TF, the Gerschgorin disk criterion is modified to estimate NTA, and the multiple signal classification (MUSIC) is exploited to estimate DOA for MIMO system. Simulation results indicate that the proposed scheme based on GMTFD has good robustness to non-Gaussian interference without prior information and that it can achieve high estimation accuracy and resolution at low and medium signal-to-noise ratios (SNRs). [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surface (RIS) is envisioned to be a promising solution to enhance coverage and capacity of future wireless networks. With the deployment of multiple RISs, the optimal placement and topology are crucial issues that need to be addressed, which are significantly different from that of active BSs/relays. This paper considers a mmWave MIMO system aided by multiple RISs in a circular layout to observe the effect of RIS placement and power scaling law. The active and passive beamforming are configured to combine the signals coherently to improve the spectral efficiency (SE). To this end, we investigate the effect of quantization error on the SE and derive an upper bound. It indicates that the SE loss is only affected by the number of quantization bits and numerical results show that 5-bit quantizer is sufficient to ensure an acceptable SE degradation. Furthermore, we evaluate the performance of the system by formulating the tight upper bounds on the average ergodic spectral efficiency and propose three effective schemes for RIS selection. Our simulation results verify the derivations and validate that the performance of deploying the RISs close to BS is significantly better than that of deploying the RISs randomly in the cell. [ABSTRACT FROM AUTHOR]

Multiband MIMO antenna system is very important in 5G mobile communication to meet the requirements of increasing data rate. However integrating multiple antennas in the limited space causes mutual coupling. The antenna configuration gets more intricate, if additional circuitry is installed to obtain greater isolation and hence, the occupied area on the board grows significantly, making the overall antenna design bulky. Hence, to provide dual band coverage, decent isolation and compact design, a compact MIMO antenna system is presented in this paper. The proposed system includes a T-shaped resonating structure that is employed on the side-frames to form a MIMO system with eight antennas. The T-shaped strip is designed to resonate at two frequencies and thereby covering two 5G bands (LTE 42/LTE 46). The proposed dual band MIMO system is fairly compact as the individual antennas are mounted on the 150 × 3.6 mm 2 side-frames of the smartphone. The isolation between antenna elements is 14 dB. This is accomplished without the need of a decoupling structure or a complex tuning process. The ergodic channel capacity realized by the suggested MIMO system in the operational bands is more than 41 b/s/Hz and ECC is less than 0.075. The impacts of the presence of plastic frame, display panel and phantom hand on the performance of proposed design are discussed. The proposed design provides compactness, dual band coverage and high isolation. [ABSTRACT FROM AUTHOR]

In this paper, a recursive d -step-ahead predictive control scheme based on multi-dimensional Taylor network (MTN) is proposed for the real-time tracking control of multiple-input multiple-output (MIMO) nonlinear systems with input time-delay. The MTN predictive model is designed using a recursive approach to compensate the influence of time-delay, and an extended Kalman filter (EKF) is applied as its learning algorithm. An MTN controller is developed based on a proportional–integral–derivative (PID) controller where the closed-loop errors between the reference input and the system output are set as the MTN controller's inputs. Then, a back propagation (BP) algorithm, designed to update its weights according to errors caused by system uncertainty, is used as a learning algorithm for the MTN controller. Meanwhile, the convergence of the MTN predictive model and the stability of the closed-loop system are evaluated. Two numerical examples and a practical example – continuous stirred tank reactor (CSTR) process are presented to verify the superiority of the proposed scheme. The experimental results and the computational complexity analysis show that the proposed scheme is effective, promising its desirable robustness, anti-disturbance, tracking and real-time performance. [ABSTRACT FROM AUTHOR]

Multi‐user (MU) massive multiple input multiple output (mMIMO) is considered a potential technology for fifth generation (5G) and sixth‐generation (6G) wireless systems. The presence of the antenna arrays at the base station level to communicate with the users or to serve tens of single antenna users leads to excessively high system costs and power consumption. The deployment 1‐bit digital‐to‐analogue converters (DACs) in the base station can solve these problems. This paper starts by presenting an analytical study centered on the effects of 1‐bit DACs on the system envisaged for a Rayleigh‐type fading channel. Compact‐form expressions are derived for the symbol error rate. Afterwards, an efficient end‐to‐end deep learning technique to compensate for the joint effect of 1‐bit DAC and imperfect channel state information in downlink mMIMO systems. Moreover, to improve the performance of the considered system, a DAC mixed architecture is proposed, where a number of antennas use 1 bit DACs while the others do not. The simulations results showed the improvement in transmission quality of the downlink of the MU‐mMIMO system in the presence of hardware imperfections using the considered end‐to‐end compensation technique. [ABSTRACT FROM AUTHOR]

Signal detection turns out to be a critical challenge in massive MIMO (m-MIMO) system due to the deployment of large number of antennas at the base station. Although, the minimum mean square error (MMSE) is one of the popular signal detection method, but, it requires matrix inversion with cubic complexity. In order to reduce computational complexity, several suboptimal signal detection methods were proposed such as Gauss-Seidel, successive over relaxation, Jacobi, Richardson methods. Although, these methods provide low complexity but their performance are limited to MMSE method. In this paper, we have proposed two signal detection techniques namely QR decompositions (QRD) and ordered QRD (OQRD). Finally, the performances of proposed signal detection methods are compared with various conventional methods in terms of symbol error rate (SER) and computational complexity. The simulation results validate that the proposed methods outperform the MMSE method with substantially lower computational complexity. [ABSTRACT FROM AUTHOR]

In this paper, we define the phase response for a class of multi-input multi-output (MIMO) linear time-invariant (LTI) systems whose frequency responses are (semi-)sectorial at all frequencies. The newly defined phase subsumes the well-known notion of positive real systems and is closely related to the notion of negative imaginary systems. We formulate a small phase theorem for feedback stability, which complements the small gain theorem. The small phase theorem lays the foundation of a phase theory of MIMO systems. We also discuss time-domain interpretations of phase-bounded systems via both energy signal analysis and power signal analysis. [ABSTRACT FROM AUTHOR]

The paper expounds the question of echo state network‐based fixed‐time adaptive tracking control for a type of multi‐input multi‐output nonlinear strict‐feedback system with input delayed saturation. Based on the feature that echo state network can obtain better estimation performance at lower computational cost, it is utilized to approximate the unknown nonlinear function in the controller design course. By constructing an auxiliary system, a time‐delay system with input saturation is eliminated. Moreover, command filtering techniques can be applied to avoid the "complexity explosion" problem in the backstepping method. Then, the closed‐loop system is attested to be semi‐global practical fixed‐time stable. In the end, a simulation shows the valid scheme. [ABSTRACT FROM AUTHOR]

This paper proposes a high-performance receiver for underwater acoustic communications based on time reversal processing for multiple-input multiple-output (MIMO) systems. The receiver employs the vector approximate message passing (VAMP) algorithm as a soft equalizer in turbo equalization. By performing self-iteration between the inner soft slicer and the inner soft equalizer, the VAMP algorithm achieves near-optimal performance. Furthermore, an iterative channel-estimation-based soft successive interference cancellation method is incorporated to suppress co-channel interference in the MIMO system. Additionally, the introduction of passive time reversal technology can combine multiple channels into a single channel, which greatly reduces the computational complexity of the MIMO system, especially for large MIMO systems. The effectiveness of the proposed receiver is verified using experimental data collected in Songhua Lake, China in 2019. The results demonstrate that the proposed receiver significantly reduces the complexity of the traditional parallel-VAMP receiver without sacrificing performance and outperforms other receivers of the same type. Moreover, our experimental results also verify that the VAMP-turbo outperforms the generalized approximate message passing (GAMP)-turbo in terms of bit error rate and convergence performance. [ABSTRACT FROM AUTHOR]

Self-energy recycling cooperative communication, in which the relay nodes simultaneously harvest energy from the source and their own transmitted signal, has been demonstrated to significantly increase the total harvested energy. In this paper, we present an optimization framework for the resource allocation and relay selection with the objective of maximizing the sum-throughput in afull-duplex multi-relay multi-user multiple-input-multiple-output network by incorporating self-energy recycling at the relays, for the first time. The formulated problem is a mixed integer non-linear programming problem, which is difficult to solve for the global optimal solution in polynomial-time. As a solution strategy, we decompose the optimization problem into two sub-problems: resource allocation problem and relay selection problem. First, we solve the resource allocation problem for a predetermined relay selection by using Taylor series approximation and convex optimization techniques. For the relay selection problem, we propose a polynomial-time sub-optimal algorithm based on the idea of iteratively selecting the relay offering the maximum throughput at each time. Through simulations, we demonstrate that the proposed dynamic time FD-MIMO system increases the throughput by 30% as compared to the conventional static time protocol system, and by a factor of 2.4 over the system without self-energy recycling. [ABSTRACT FROM AUTHOR]

In this paper, we propose the application of a new geometric procedure in order to calculate a set of transmission zeros of a propagation environment. Since the transmission zeros play a crucial role in modern communication systems, there is a need to apply the efficient solutions characterized by a maximum speed operation. It turns out that the classical method based on the Smith–McMillan factorization is time-consuming, so its contribution to the detection of transmission zeros could be unsatisfactory. Therefore, in order to fill the gap, we present a new algorithm strictly dedicated to the multivariable telecommunications systems described by the transfer-function approach. Consequently, a set of new achievements resulted, particularly in terms of computational efforts. Indeed, the proposed procedure allows us to overcome obstacles derived from technological limitations. The representative simulation examples confirm the great potential of this new method. Finally, it has been pointed out that the newly introduced geometric-originated approach has significantly reduced the computational burden. Indeed, for the randomly selected matrix of the 5 × 5 dimension describing the sensor-related propagation environment, two representative scenarios were performed in order to manifest the crucial properties. In the first scenario, the sets of multiple transmission zeros were analyzed, ultimately leading to intriguing results. The Smith–McMillan solution took three times longer to discover the mentioned sets. On the other hand, the second instance brought us the same result. Naturally, the discussed difference has increased as a function of the number of matrix elements. For the square matrices involving 100 components, we have observed the respective differences, both over Q I = 100 and Q I I = 60 . It should be emphasized that the finding derived from the Smith–McMillan factorization corresponds to the geometric-related approach in the context of some mechanisms. This is particularly visible when appointing the greatest common divisors. [ABSTRACT FROM AUTHOR]

A multiple-input-multiple-output (MIMO) antenna system with low-profile and small element spacing characteristics is presented in this paper. This antenna contains multiple elements arranged in both E-plane and H-plane configurations. The original strong coupling between the MIMO elements can be suppressed by exciting orthogonal operating modes. To achieve this, a half-wavelength microstrip line and a quarter-wavelength grounded stub are utilized to decouple the H- and E-plane MIMO arrays. A 2 × 2 antenna prototype is fabricated and measured to demonstrate the decoupling concept's feasibility. The measured impedance bandwidth is from 4.78 to 4.81 GHz. Across this band, the isolation is better than 15 dB with extremely small edge-to-edge distances of 0.032λ and 0.026λ in the E- and H-plane, respectively. Featuring the simple decoupling structure, small element spacing, and the capability of extending to a large-scale 2 × N array, the proposed antenna can be used for 5G Internet of Things (IoT) applications operating at the N79 frequency band. [ABSTRACT FROM AUTHOR]

This paper shows that the signal‐to‐interference‐plus‐noise ratio (SINR) at a base station (BS) equipped with an arbitrary physical array antenna can be expressed as a function of two fundamental, stochastic figure of merits (FoMs): (I) the instantaneous effective gain (IEG) and (II) the beamforming‐channel correlation (BCC). This result is achieved by applying a novel channel normalization approach using a reference array to preserve effects induced by the embedded element patterns of physical antenna elements. It is shown that both FoMs provide essential insights for quality‐of‐service (QoS)‐based array antenna design by investigating their statistics for BSs applying full‐digital (FD) zero forcing (ZF) beamforming. Various array designs are evaluated, and it is shown that arrays with higher IEGs and a reduced probability of low BCCs can increase the ergodic sum rate and reduce the need for scheduling. [ABSTRACT FROM AUTHOR]

The attachment of 5G with millimeter wave (mmWave) frequencies offers massive capacity and low latency to reveal the full 5G experiences. High directive gain and beamforming are considered essential for mmWave 5G systems. The main requirements of the beamforming network for 5G mmWave applications are the scanning coverage of ± 60° and SLL > 10 dB in a wide operational bandwidth over the standard 5G frequency bands. In this paper, a novel PCB-based wide-angle Rotman lens beamformer is designed, simulated, and successfully measured to meet the mentioned requirements for 5G mmWave applications. A comprehensive improved design methodology is provided for all components of the Rotman lens to reach a wide scanning angle, enhanced sidelobe level, and low scan loss. The end-fire Vivaldi antenna is selected as an array element for easy integration to the beamforming network as well as its capability to use in dual-polarization configuration. The proposed Rotman lens is operational in the 24–30 GHz frequency band covering 5G n257, n258, and n261 frequency bands. The results show a nearly constant 8 beams across the whole bandwidth steering from − 53° to 53° in 15° increments to provide ± 60° coverage with the SLL > 10 dB and scan loss < 1.9 dB. The retrieved novelties from this work contain an effective design methodology for an optimized Rotman lens with wide-scan angle and low phase and amplitude error, non-uniform distribution based array ports, and integration with end-fire antenna for possible dual polarization and 2-D beamforming capabilities. The comparison of the proposed beamformer with the most recent works shows several advantages in terms of integrated structure and performances including bandwidth, wide scanning angle, SLL, and scan loss. With such performances, this beamformer can be used for various mmWave and 5G applications such as advanced antenna systems, massive MIMO systems, and hybrid beamforming systems. [ABSTRACT FROM AUTHOR]

The multiple-input multiple-output synthetic aperture radar (MIMO SAR) system has developed rapidly since its discovery. At the same time, the low-disturbance and high-gain requirements of the MIMO system are continuing to increase. Through the application of digital beamforming (DBF) techniques, the multidimensional waveform encoding (MWE) technique can play a key role in MIMO systems, which can greatly improve the system's performance, especially the multi-mission capability of radar. Intrapulse beamsteering in elevation is a typical form of multi-dimensional waveform encoding which can greatly improve the transmitting efficiency and multi-mission performance of radar. However, because of the high sensitivity of the DBF technique to height, there is significant deterioration in performance in the presence of terrain undulations. The OFDM (Orthogonal Frequency Division Multiplexing) technique is widely used in communication. Due to the similarity of radar and communication systems and the great waveform diversity of OFDM signals, the OFDM radar has recently begun to emerge as a new radar system, simultaneously, the orthogonality of OFDM signals is in the time and frequency domains, and is not affected by terrain undulation. So, this paper proposes a novel radar mode combining intrapulse beamsteering in elevation and OFDM-Chirp signals, that is, the combination of "beam orthogonality" and "waveform orthogonality", which can greatly improve the performance and fault tolerance to interference signals. In this manuscript, the system working mode and signal processing flow are introduced in detail, and simulations for both point targets and distributed targets are carried out to verify the feasibility of the proposed mode. Simultaneously, a comparison experiment is carried out, which shows the high level of fault tolerance to terrain undulation and the high potential of the proposed radar mode in Earth observation. [ABSTRACT FROM AUTHOR]

This paper presents a dual‐band multiple‐input multiple‐output (MIMO) antenna pair for 5G cell phones. The antenna pair consists of two planar concave antenna elements that are tightly arranged on a vertical substrate. By adding capacitors to form a resonant network, an additional operating mode is excited, resulting in dual‐band performance. The resonant frequency of the additional mode can be adjusted independently by changing the capacitance value. Moreover, a neutralization line is used to improve the isolation of the high band without affecting the low band. The antenna pair occupies a small area of 13.2 × 6.3 mm2 and operates at both 3.5 and 4.85 GHz bands. The isolations are better than 17 dB at the 3.5 GHz band and better than 19 dB at the 4.85 GHz band. The proposed dual‐band antenna pair might be a good choice for 5G terminals. [ABSTRACT FROM AUTHOR]

New possibilities of Gramian computation, by means of canonical transformations into diagonal, controllable, and observable canonical forms, are shown. Using such a technique, the Gramian matrices can be represented as products of the Hadamard matrices of multipliers and the matrices of the transformed right-hand sides of Lyapunov equations. It is shown that these multiplier matrices are invariant under various canonical transformations of linear continuous systems. The modal Lyapunov equations for continuous SISO LTI systems in diagonal form are obtained, and their new solutions based on Hadamard decomposition are proposed. New algorithms for the element-by-element computation of Gramian matrices for stable, continuous MIMO LTI systems are developed. New algorithms for the computation of controllability Gramians in the form of Xiao matrices are developed for continuous SISO LTI systems, given by the equations of state in the controllable and observable canonical forms. The application of transformations to the canonical forms of controllability and observability allowed us to simplify the formulas of the spectral decompositions of the Gramians. In this paper, new spectral expansions in the form of Hadamard products for solutions to the algebraic and differential Sylvester equations of MIMO LTI systems are obtained, including spectral expansions of the finite and infinite cross - Gramians of continuous MIMO LTI systems. Recommendations on the use of the obtained results are given. [ABSTRACT FROM AUTHOR]

Hybrid precoding is an emerging solution for millimeter wave massive MIMO system to achieve reduced complexity and enhanced spectral efficiency. Hybrid precoding fully exploits spatial information to achieve high channel gain which reduces excessive path loss. The research presented in this paper is aiming at the design of optimal precoder which is based on Logarithmic hyperbolic cosine cost function with ZA and RZA criterion and has the advantage of reduced RF Chains. The channel state information (CSI) is generated by using random values of angle of arrival (AoA) and angle of departure (AoD) information. This CSI information is crucial to generate the precoding matrices and the row wise elements of the digital precoding matrix are recursively updated by using hyperbolic cosine filtering algorithm. Proposed precoder achieves spectral efficiency of 13 bps/Hz with 20 dB SNR which is comparatively higher than the precoders designed using other state of art methods. The performance is tested by varying base station antennas, mobile station antennas and number of users. The achievable spectral efficiency with variation of antenna elements and users establish the superior performance of the precoder. Thus the design can be suitable for fifth and next generation wireless communication systems when higher data rate is the requirement in millimeter wave band. [ABSTRACT FROM AUTHOR]

Massive multiple-input multiple-output (MMIMO) is a WiFi access technique studied and investigated in response to the worldwide bandwidth bottleneck in the WiFi telecommunication industry. Massive MIMO, which brings multiple antennae to transmission and reception to deliver excellent spectrum and power effectiveness with comparatively simple computation, is among the leading fundamental technologies for next-generation networking. For such a practical implementation of 5G—and further, that networks will realize many implementations of the smart sensor device—it is essential to gain a greater understanding of such a massive MIMO model to address its underlying problems. Because of the significant achievements of reinforcement learning (RL) and deep learning (DL), new and potent techniques are now available to help MIMO telecommunication networks deal with problems. This paper presents a thorough analysis of the convergence among the two fields, emphasizing RL and DL methods for MIMO networks. Throughout this article, a framework for RL-based beam-forming vector assault defense has been presented (reinforcement learning). Its outcomes demonstrated acceptable efficiency as well as the anticipated outcome. [ABSTRACT FROM AUTHOR]

This paper presents a hierarchical identification model for multiple-input multiple-output (MIMO) systems. An auxiliary model-based hierarchical stochastic gradient (AM-HSG) algorithm is derived by means of the auxiliary model identification idea and the hierarchical identification principle. Furthermore, an auxiliary model-based hierarchical multi-innovation stochastic gradient (AM-HMISG) algorithm is derived by utilizing the multi-innovation identification theory. In order to compare the computational efficiency of the AM-HSG and AM-HMISG algorithms for identifying MIMO systems, this paper gives the existing traditional gradient algorithms and discusses the complexity of these algorithms in detail. Finally, the simulation example tests the effectiveness of all four algorithms. [ABSTRACT FROM AUTHOR]

We propose a data-driven robust iterative learning control (ILC) technique to multi-input-multi-output (MIMO) linear systems. Control of MIMO linear systems, particularly with strong cross-axis coupling, is challenging as modeling of a MIMO system can be complicated, time-consuming, and often requires a trade-off between robustness and performance. As such, limitations exist in current ILC techniques. The aim of this paper is to develop an efficient and easy-to-use data-driven ILC technique to output tracking of MIMO linear systems under random disturbance. Through the proposed technique, the complicated modeling process and the robustness-accuracy trade-off are avoided, and the up-to-now system dynamics is captured by constructing and updating the iteration gain using the input and output data in the last iteration. It is shown that monotonic convergence of the ILC algorithm is guaranteed, and an optimal gain can be obtained to maximize the convergence rate and minimize the residual tracking error. The proposed technique is illustrated through experiments on a three-input three-output piezoelectric actuator system, with comparison to the adaptive multi-axis inversion-based iterative control (A-MAIIC) technique. The experimental results show rapid convergence and improved formance of the proposed technique when the cross-axis coupling is strong. [ABSTRACT FROM AUTHOR]

Localization models, particularly for indoor applications like autonomous vehicles and smart manufacturing in Industry 4.0, have emerged as a crucial research area because of the growing need for precise positioning. In this context, distributed cell-free MIMO have demonstrated capabilities for a higher fine-grained resolution and accuracy. This paper explores different approaches to address challenges related to positioning and corresponding data transmission and model compression problems in cell-free massive MIMO systems. We present a novel deep-learning powered CSI compression model for reducing the signalization data associated with the localization task, minimizing the fronthaul capacity requirement by at least a factor of 2, without a significant loss of accuracy. In our research, we also focus on the challenges of deploying deep end-to-end neural networks on resource-constrained embedded platforms. Two approaches are proposed: model modification and magnitude-based pruning. Our results show that the pruned end-to-end localization model has a smaller size by 36% at the cost of an increase on the localization error by a factor of 1.5 (precision is reduced from approximately 5 mm to 7.8 mm). [ABSTRACT FROM AUTHOR]

Dual-function radar and communications (DFRC) systems based on multiple-input multiple-output (MIMO) arrays have received considerable attention in recent years due to their excellent ability to alleviate spectrum congestion. The MIMO DFRC systems enable high-resolution detection of multiple targets while communicating with multiple users simultaneously. However, MIMO arrays require numerous radio frequency (RF) units and suffer a strong mutual coupling among antennas, resulting in significant system overhead and performance degradation, respectively. In light of this, this paper investigates the joint optimization of transmit precoding and antenna selection for MIMO DFRC systems, aiming to improve the angular ambiguity function with a guaranteed communication quality of service (QoS) using a small number of antennas. To address the resultant non-convex optimization problem, both the indirect and direct precoding methods are proposed. In the former, the waveform covariance and antenna selection vector are first jointly optimized via a promoted sparsity along the covariance diagonal, followed by the precoding matrix indirectly derived from the optimal covariance. In the latter, the precoding matrix is directly optimized via an imposed group sparsity under the communication QoS and power constraints. Simulation results demonstrate that the proposed sparse MIMO DFRC system with fewer active antennas can achieve comparable dual-functional performance to that of the full array system. • The joint design of precoding and antenna selection for MIMO DFRC is proposed. • The indirect precoding method is proposed to address the joint optimization problem. • The direct precoding method is proposed to directly optimize the precoding matrix. • The proposed sparse system achieves comparable performance to that of a full system. [ABSTRACT FROM AUTHOR]

This paper investigates a high-order disturbance observer-based safe tracking control scheme for a class of uncertain multiple-input and multiple-output systems under time-varying full state constraints and disturbances. To achieve the safe tracking objective, a boundary protection algorithm is introduced to generate new safe desired signals which are within corresponding state constraints. An improved second-order dynamic surface control technology is developed to deal with the piecewise differentiability of safe desired signals and the phenomenon of repeatedly differentiation, simultaneously. To handle the negative effects of system uncertainties and obtain better estimation effect of high order time-varying disturbances, the radial basis function neural networks and high-order disturbance observer methods are developed. The safety and performance of the closed-loop nonlinear system under the proposed control scheme have been rigorous proved and discussed by Lyapunov stability analysis. Finally, a two-link manipulator model has been given as an example, and the numerical simulations are given to express the availability of the proposed controller. • Different with the high-gain principle of common constrained control methods, a boundary protection algorithm (BPA) has been proposed to calculate new safe desired signals, which can solve the full-state constrained control problem with the time-varying and unpredicted state constraints. • A novel auxiliary system and a second-order filter (SOF) technique have been developed to solve the problem caused by BPA. Comparing with conventional filter in dynamic surface control [13], this SOF can reach the unbiased estimation of the time derivative of the input signal. • The HODO-based safe tracking control scheme is able to realize the safety and convergence of the higher-order, under-actuated nonlinear system under high frequency disturbances proved by rigorous Lyapunov analysis, and the restricted conditions have been strictly discussed. [ABSTRACT FROM AUTHOR]

• A multi-input, multi-output(MIMO) adaptive active vibration isolation algorithm is proposed to address the impact of the unbalanced input perturbation problem on the convergence of the adaptive algorithm. • The constructed form of the algorithm is given by filtering the input and output of the system. • The convergence of the algorithm is verified and the construction of the weighted transformation matrix is given. • The vibration isolation performance and convergence of the algorithm are verified by simulation. Multi-axis active vibration isolation systems often encounter order-of-magnitude differences in the perturbations in each input direction. This causes adaptive feedforward control systems to converge at a relatively slow rate, or possibly even diverge. To solve this problem, we propose an improved adaptive feedforward vibration isolation technique based on the classical FxLMS algorithm. Our approach constructs a weighted transformation matrix based on singular value decomposition, attenuates the uneven influence of the input perturbations, and effectively avoids the problems of slow convergence and poor stability faced by existing adaptive algorithms. This paper first describes the parameter update algorithm and the structure of adaptive feedforward algorithms. The convergence of the proposed algorithm is then analyzed, and the method for constructing the weighting matrix is introduced. Finally, the feasibility of the algorithm is verified using a multi-axis vibration isolation system simulation model. The simulation results show that the power spectrum of acceleration in the frequency domain from 0.1 to 100 Hz is attenuated by up to 70 dB under the condition of no excitation in a specific direction, demonstrating the vibration isolation effect of the algorithm. The relative deviation between the predicted and actual parameter error is no more than 6 %, which shows that the algorithm achieves good convergence. The proposed algorithm has potential applications in the active vibration isolation of space telescopes and high-precision optical equipment. [ABSTRACT FROM AUTHOR]

This paper presents a multi-rate digital control scheme for a class of nonlinear systems where the controller and observer are both designed in discrete time and where the plant has zero dynamics and is multiple input, multiple output (MIMO). The fast observer is a discrete time analogue to the high gain observer, which can achieve quick estimation of states. Performance recovery is achieved with the fast observer and control saturation. [ABSTRACT FROM AUTHOR]

The integration of microwave and mm-wave antenna is recognized as a promising solution to ensure backward compatibility while effectively utilizing available space for 5G-enabled modern IoT devices. This paper proposed a very compact size multi-band dual-port MIMO antenna module, aiming to provide complete coverage in microwave frequency bands, including 2.4 GHz, 3.5 GHz, 5.3 GHz, and 7.5 GHz, along with 5G mm-wave bands such as 28 GHz. This is achieved through a common feedline by incorporating a semi-circular slotted monopole with strategically positioned quarter-wavelength metallic stubs on a partial ground plane. The proposed antenna exhibited -10 dB impedance bandwidths of 22.6% (2.16–2.71 GHz), 17.9% (3.36–4.02 GHz), 9.4% (5.18–5.69 GHz), 15.9% (7.29–8.55 GHz), and 15.5% (26.02–30.39 GHz) across diverse frequency bands. To attain compactness and high isolation capabilities, a composite three-dimensional parasitic element is employed as a decoupling structure to alleviate significant interference among closely positioned radiating elements. The proposed antenna provides radiation efficiency of > 85%, achieving necessary peak gains of 2.32 dBi, 3.46 dBi, 4.15 dBi, and 5.32 dBi at respective microwave frequencies. While in mm-wave frequency bands, radiation efficiency exceeds 95% having a measured realized gain of 6.2 dBi at 28 GHz. The proposed design successfully maintains an average isolation of > 28 dB in all frequency bands. The suggested MIMO configuration demonstrates outstanding diversity performance, which is evidenced by ECC, DG, MEG, CCL, and TARC. [ABSTRACT FROM AUTHOR]

In this paper, an eight-element multiple-input multiple-output (MIMO) antenna with dual-band response is designed for fifth-generation (5G) mobile devices. The proposed MIMO systems' radiators are placed in the inner side frame of the mobile phone board, which has a size of 150 × 7 mm 2 , each spanning an area of 21.5 × 5.5 mm 2. All eight antenna elements are symmetrical to each other and are fed with a 50 Ω feeding line. The proposed MIMO antenna covers two distinct 5G frequency bands, i.e., 3.5 and 5.3 GHz, and offers −6 dB impedance bandwidth in a frequency range of 3.4–3.65 and 4.8–5.8 GHz. It is worth mentioning that an isolation of more than 18 dB is achieved over the bands of interest without the use of a decoupling network. Similarly, the proposed MIMO antenna achieved an efficiency of more than 60% across the bands, an envelope correlation coefficient (ECC) below 0.04, antenna gain better than 5 dBi, a mean effective gain (MEG) between any two radiating elements found to be better than 3 dB, and a channel capacity (CC) above 38.5 bps/Hz, which is three times better compared to a 2 × 2 MIMO antenna system. Upon fabrication and testing of the proposed antenna, it was noted that the measured results are in good agreement with the simulated data. Furthermore, the MIMO antenna response was simulated in the presence of a human user and shows how the user phantom affects the radiation characteristics of the MIMO antenna. These performance parameters showed promising results, making the proposed design a better option to be deployed in modern-day 5G communication application scenarios, specifically cell phones. [ABSTRACT FROM AUTHOR]

Accurate channel estimation at the base station (BS) is crucial for reliable, high-throughput communication in massive multiple input multiple output (MIMO) systems. However, the process of estimating channel matrices in massive MIMO systems is prone to pilot contamination attacks. These attacks not only degrade the efficiency of channel estimation but also increase the probability of information leakage. In this paper, a method is being proposed for detecting pilot contamination signals using a deep learning model, namely a generative adversarial network (GAN), at the BS of a massive MIMO system. GAN comprises two interconnected models, the first model is the generator, which creates synthetic samples, and the second model is the discriminator, which distinguishes between real and generator-generated samples. Residual block-based convolutional neural networks (CNNs) like ResNet and reverse ResNet are implemented as discriminator model of GAN for improving the detection of pilot-contaminated signals. The generator, structured as an autoencoder, generates artificial data resembling real data from noisy signals. The simulation results demonstrate that the proposed model effectively identifies the pilot-contaminated attacks; this effectiveness is evaluated through performance matrices called accuracy and loss function. Furthermore, the performance of the model is observed by varying the number of BS antennas and pilot lengths. [ABSTRACT FROM AUTHOR]