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The proposed Minkowski fractal antenna design achieves wideband and continuous frequency tuning in a multiple-input and multiple-output (MIMO) antenna system. By manipulating the fractal geometry of the unit antenna element, the resonance frequency of the antenna can be adjusted simply by changing its electrical length. The Minkowski fractal operator generates an increasing current path, resulting in a leftward frequency shift as the antenna side length increases with each iteration. In the first iteration, the proposed fractal antenna demonstrated a 97.9% continuous frequency shift from 0.204 to 0.404 THz with maximum return loss values of −31.23 and −21.6 dB, respectively. In the second iteration, a 38.6% continuous resonance frequency shift from 0.413 to 0.578 THz was achieved with return loss values of −18.22 and −40.47 dB, respectively. The maximum tunable bandwidths of the first and second iterations were approximately 0.2 and 0.16 THz, respectively. The proposed correlation between the dimensions of a single antenna and its resonance frequency provides the foundation for designing and implementing MIMO antenna systems in high-speed wireless devices, cognitive radio, and other multiband MIMO applications. A 2 × 2 MIMO antenna system has been designed from the results of the proposed single antenna to achieve multiband operation or frequency tuning through selective switching of the antenna feed. [ABSTRACT FROM AUTHOR]

Multiple-input multiple-output (MIMO) technology is employed to improve the reliability and capacity of wireless communication systems. However, the wireless communication environment creates vulnerabilities to spoofing attacks. Furthermore, the authentication challenges posed by the heterogeneous characteristics of wireless applications increase as diverse technologies facilitate the growing number of Internet of Things (IoT) devices. To address these challenges, adaptive physical-layer authentication (PLA) leveraging the inherent antenna diversity in MIMO systems is examined, and an informationtheoretic perspective on PLA in MIMO systems is given. The real and imaginary components of the received reference signals are used as attributes with a singleclass classification support vector machine (SCC-SVM). It is shown that the authentication performance improves with the number of antennas, and the proposed scheme provides robust authentication. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surface (RIS) can provide unprecedented spectral efficiency gains and excellent ability to manipulate electromagnetic waves. This article considered a RIS‐assisted multiuser multiple‐input multiple‐output (MIMO) downlink system, where the beamforming at the base station and RIS are jointly designed to maximize the sum‐rate. For the large dimension scenario and high‐rank beamforming matrix, the accurate deterministic approximations from random matrix theory are then utilized to simplify the RIS‐assisted MIMO systems. The asymptotical signal‐to‐interference‐plus‐noise ratio values obtained through random matrix theory is infinitely close to the theoretical limits calculated by accurately iteration. And the performance of the proposed algorithm computed via the sharing second‐order channel statistics matches that of the RIS algorithm with sharing full channel state information asymptotically. The deterministic approximations are instrumental to get improvement into the structure of the optimal beamforming and to reduce the implementation complexity in large‐scale MIMO system. Numerical simulations results are provided to evaluate and verify the accuracy of the asymptotic results obtained from the proposed algorithm in the finite system regime. With the complex operation process of large dimension matrix reducing to the deterministic approximations, a lower computational complexity can be obtained compared with other methods. [ABSTRACT FROM AUTHOR]

For uncertain multiple‐inputs multi‐outputs nonlinear systems, it is nontrivial to achieve asymptotic tracking due to the intrinsic coupling among inputs, while the controllability conditions in most existing methods are rather restrictive or even impractical especially when unexpected actuator faults are involved. In this article, we focus on extending such controllability condition by resorting to the existence (instead of a priori knowledge) of some feasible auxiliary matrix, upon which a robust adaptive control scheme is first presented in the absence of actuator faults that is not only able to achieve asymptotic tracking even in the presence of non‐parametric uncertainties with all the closed‐loop signals globally ultimately uniformly bounded, but also able to deal with a larger class of system models. Furthermore, for the case with intermittent actuator faults, we develop a fault‐tolerant control scheme with extended condition for controllability that is able to accommodate such faults automatically without using any fault detection or fault diagnosis unit. The effectiveness and benefits of the proposed method are verified via simulation on robotic systems. [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]

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]

The coupled systems with numerous interdependent variables pose considerable control and stability issues in commercial chemical processes. These systems exhibit complex interactions, time delays, parameter uncertainties and disturbances, which can lead to poor performance and reduced productivity. Conventional single-loop controllers struggle to effectively manage the interactions between variables, leading to poor disturbance rejection and tracking accuracy. A more robust and efficient control strategy is essential to tackle these limitations. Hence, a robust decentralized PID controller design is proposed to address the shortcomings of coupled industrial chemical processes. The aim is to enhance control system performance by achieving better disturbance rejection and tracking accuracy, thereby improving process efficiency and product quality. The complementary sensitivity function is exploited to attain this, and a novel graphical tuning method is proposed for obtaining optimal PID controller parameters. The use of the H ∞ robust criterion ensures robust stability and minimizes the influence of external disturbances. The proposed approach offers an efficient means to design robust decentralized controllers for complex chemical processes. The simulation results demonstrate the effectiveness of the method compared to existing controllers. Numerically, the proposed controller is able to attain almost a 20 % reduction in integral absolute error and a 30 % decrease in the settling time for all cases compared to existing controllers. The graphical tuning method enables efficient parameter optimization, resulting in faster response times and reduced settling time. This work represents a significant step toward achieving enhanced control system performance and stability in coupled industrial chemical processes. [ABSTRACT FROM AUTHOR]

This paper investigates command filter‐based finite‐time stability of multi‐input multi‐output (MIMO) dynamic systems with prescribed performance constraints and external disturbances. A novel finite‐time differentiator is introduced into command filter‐based control scheme, which improves transient performance of each subsystem. Meanwhile, disturbance observers are utilized to eliminate negative effects on control system caused by external disturbances. Furthermore, featured with a selected performance function, it can be guaranteed that tracking errors remain in prescribed performance region. Stability analysis of the proposed controller is presented by using a Lyapunov function including transformed filter errors, parameter errors of neural networks, and observed errors of lumped disturbances. Effectiveness of proposed control method is verified by a numerical example and a practical system of inverted pendulums, respectively. [ABSTRACT FROM AUTHOR]

Summary: Multiple‐input multiple‐output (MIMO) technology can boost the capacity of conventional satellite communications, and full frequency reuse (FFR) can further improve the capacity by making full use of frequency resources. However, FFR will cause severe co‐channel interference and lead to a degradation of channel capacity. Blind source separation (BSS) algorithm can be used to eliminate co‐channel interference and does not require prior information, which avoids the extra occupation of channel frequency resources. While the multi‐satellite MIMO communication system have delay mixing characteristic, the separation performance will be degraded if the delay is ignored. In this paper, DMBSS algorithm for delay mixing is proposed. Firstly, the DMBSS algorithm utilizes Taylor expansion to achieve dimension expansion of observed signals and so as to transform the delay mixing into instantaneous mixing with extended dimension. Secondly, complex non‐orthogonal joint diagonalization algorithm is used to solve the separation matrix equation of extended observation signals and extended source signals and thus to eliminate the co‐channel interference. Simulation results show that for multi‐satellite MIMO communication scenarios with FFR, the proposed algorithm can effectively improve the performance of co‐channel interference cancellation and reduce the value of BER. Furthermore, the influence of order of Taylor expansion on the separation performance is also analyzed and simulated in this paper to show the cost performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Summary: Millimeter‐wave (mmWave) massive MIMO and non‐orthogonal multiple access (NOMA) are promising ways for improving energy efficiency of integrated terrestrial‐satellite networks. In this paper, we investigate the performance of integrated terrestrial‐satellite networks, in which MIMO‐NOMA‐based terrestrial networks and satellite network cooperatively provide ubiquitous services for ground users while reusing the entire bandwidth. Both base stations (BSs) and satellite are equipped with multiple antennas, and beamforming technology is utilized to serve multiple users simultaneously. First, a user selection scheme based on channel gain ratio is proposed to select satellite users. Then, we design a user clustering algorithm for reducing the intra‐beam and inter‐beam interference of BSs users. Analog and digital beamforming techniques are also adopted to further increase beam gain and improve system energy efficiency. The terrestrial networks and satellite networks energy efficiency are separately optimized through beamforming design and power allocation scheme, and then, a joint iterative algorithm is proposed to maximize the system energy efficiency. In addition, we introduce the interference temperature threshold to limit the satellite interference to BSs users. Finally, the performance of the proposed algorithm is simulated in comparison with the existing algorithm. The results indicate that the presented algorithm has high superiority in system energy efficiency and it can be applied to integrated terrestrial‐satellite networks. [ABSTRACT FROM AUTHOR]

A three-stage decentralized controller design algorithm is developed to achieve setpoint tracking and disturbance rejection in MIMO systems with communication time delay, and nonlinearities such as saturation and dead band. The first stage involves reference model formulation. The second stage comprises equating approximate generalized time moments/approximate generalized Markov parameters of closed-loop system model with reference model at certain expansion points in s-plane to obtain synthesis-like equation, concerning products of unknown controller numerator and denominator polynomials. This process yields simultaneous linear equations, whose solution provides the coefficients of the product polynomials. In the third stage, the controller parameters are extracted from the product polynomials using exact model matching. The proposed method is illustrated by designing load frequency controller in traditional and restructured power systems under scenarios like system parameter uncertainties, random load variation, and time-varying communication delays. Simulation studies reveal the efficacy of the proposed technique over existing techniques. Furthermore, the practical implementation feasibility of the decentralized controller designed for the restructured power system is validated using the TMS320F28379D controlCARD. [Display omitted] • Model matching decentralized controller design for servo and regulatory problems. • Handles communication time delay, saturation, and dead band nonlinearities. • Need for decoupler and order reduction of MIMO system is not required. • 1/2/3-DOF controller of any structure can be designed irrespective of system's order. • Real-time validation of designed controller using TMS320F28379D controlCARD. [ABSTRACT FROM AUTHOR]

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

This study investigates receiver design solutions for distributed Massive Multiple Input Multiple Output (D-m MIMO) systems, taking into account parameters such as number of access points as well as concerns related to channel estimates that use single-carrier frequency-domain equalization (SC-FDE). A significant contribution of this research is the integration of Low-Density Parity-Check (LDPC) codes to simplify coding complexity and enhance communication efficiency. The research examines different receiver designs, such as spatial antenna correlation and sophisticated channel estimation methods. The authors propose integrating LDPC codes into the receiver architecture to simplify computations and enhance error correction and decoding. Moreover, the paper examines performance evaluation measures and approaches, highlighting the trade-offs among complexity, spectral efficiency, and error performance. The comparative analysis indicates the benefits, in terms of performance, of incorporating LDPC codes and improving system throughput and dependability. We examine four distinct receiver algorithms: zero-forcing (ZF), minimum mean square error (MMSE), maximum ratio combining (MRC), and equal gain combining (EGC). The study shows that MRC and EGC receivers work well in D-m MIMO because they make the receiver system less computationally demanding. [ABSTRACT FROM AUTHOR]

Massive multiple-input-multiple-output (M-MIMO) offers remarkable advantages in terms of spectral, energy, and hardware efficiency for future wireless systems. However, its performance relies on the accuracy of channel state information (CSI) available at the transceivers. This makes channel estimation pivotal in the context of M-MIMO systems. Prior research has focused on evaluating channel estimation methods under the assumption of spatially uncorrelated fading channel models. In this study, we evaluate the performance of the minimum-mean-square-error (MMSE) estimator in terms of the normalized mean square error (NMSE) in the uplink of M-MIMO systems over spatially correlated Rician fading. The NMSE allows for easy comparison of different M-MIMO configurations, serving as a relative performance indicator. Besides, it is an advantageous metric due to its normalization, scale invariance, and consistent performance indication across diverse scenarios. In the system model, we assume imperfections in channel estimation and that the random angles in the correlation model follow a Gaussian distribution. For this scenario, we derive an accurate closed-form expression for calculating the NMSE, which is validated via Monte-Carlo simulations. Our numerical results reveal that as the Rician K -factor decreases, approaching Rayleigh fading conditions, the NMSE improves. Additionally, spatial correlation and a reduction in the antenna array interelement spacing lead to a reduction in NMSE, further enhancing the overall system performance. [ABSTRACT FROM AUTHOR]

The multi-input multioutput (MIMO) systems involving multirelational signals generated from distributed sources have been emerging as the most generalized model in practice. The existing work for characterizing such a MIMO system is to build a corresponding transform tensor, each of whose entries turns out to be the individual z -transform of a discrete-time impulse response sequence. However, when a MIMO system has a global feedback mechanism, which also involves multirelational signals, the aforementioned individual z -transforms of the overall transfer tensor are quite difficult to formulate. Therefore, a new mathematical framework to govern both feedforward and feedback MIMO systems is in crucial demand. In this work, we define the tensor z -transform to characterize a MIMO system involving multirelational signals as a whole rather than the individual entries separately, which is a novel concept for system analysis. To do so, we extend Cauchy's integral formula and Cauchy's residue theorem from scalars to arbitrary-dimensional tensors, and then, to apply these new mathematical tools, we establish to undertake the inverse tensor z -transform and approximate the corresponding discrete-time tensor sequences. Our proposed new tensor z -transform in this work can be applied to design digital tensor filters including infinite-impulse-response (IIR) tensor filters (involving global feedback mechanisms) and finite-impulse-response (FIR) tensor filters. Finally, numerical evaluations are presented to demonstrate certain interesting phenomena of the new tensor z -transform. [ABSTRACT FROM AUTHOR]

The practical implementation of massive multi-user multi-input–multi-output (MU-MIMO) downlink communication systems power amplifiers that are energy efficient; otherwise, the power consumption of the base station (BS) will be prohibitive. Constant envelope (CE) precoding is gaining increasing interest for its capability to utilize low-cost, high-efficiency nonlinear radio frequency amplifiers. Our work focuses on the topic of CE precoding in massive MU-MIMO systems and presents an efficient CE precoding algorithm. This algorithm uses an alternating minimization (AltMin) framework to optimize the CE precoded signal and precoding factor, aiming to minimize the difference between the received signal and the transmit symbol. For the optimization of the CE precoded signal, we provide a powerful approach that integrates the majorization-minimization (MM) method and the fast iterative shrinkage-thresholding (FISTA) method. This algorithm combines the characteristics of the massive MU-MIMO channel with the second-order Taylor expansion to construct the surrogate function in the MM method, in which minimizing this surrogate function is the worst-case of the system. Specifically, we expand the suggested CE precoding algorithm to involve the discrete constant envelope (DCE) precoding case. In addition, we thoroughly examine the exact property, convergence, and computational complexity of the proposed algorithm. Simulation results demonstrate that the proposed CE precoding algorithm can achievean uncoded biterror rate (BER) performance gain of roughly 1 dB compared to the existing CE precoding algorithm and has an acceptable computational complexity. This performance advantage also exists when it comes to DCE precoding. [ABSTRACT FROM AUTHOR]

A novel aperture‐shared dual‐wideband 5G antenna with dual polarization in the sub‐6 GHz band and multi‐input multi‐output (MIMO) function in the 28‐GHz band is presented in this work. A dual‐polarized megnetoelectric dipole (ME‐dipole) with wide bandwidth is introduced in the sub‐6 GHz band. Based on the partial structure reuse method, four 2 × 2 substrate integrated waveguide (SIW) cavity‐backed patch arrays are integrated into the electric dipole structure of the ME‐dipole to construct a 28‐GHz 2 × 2 MIMO system. Two high‐order SIW cavity modes that cooperate with patch modes are used to enhance bandwidth in the 28‐GHz band. For demonstration, a prototype of the proposed antenna was fabricated and measured. The simulated results are in good agreement with the measured results, showing wide bandwidths of 39.6% and 10.85% in the sub‐6 GHz and the 28‐GHz bands, respectively. In addition, excellent measured in‐band and cross‐band isolations of over 44.4 dB and 45.2 dB are obtained, respectively. [ABSTRACT FROM AUTHOR]

The novelty of the proposed work lies in the control technique, referred to as the robust generalized dynamic inversion based adaptive recursive sliding mode control (RGDI-ARSMC), for addressing various challenges to control a highly coupled and perturbed system called twin rotor MIMO systems (TRMS) UAV. The continuous disturbances, varying parameter values, actuator failure, and unmodeled states are the challenges related to the proposed controller. The method aims to effectively mitigate unwanted signals, including coupling effects, unknown states, gyroscopic disturbance torque, parametric uncertainties, and other disturbances. The control design process is divided into two phases: the first involves estimating the deviation between the actual and desired output angles and conducting a stability phase analysis. The confined stability-based Lyapunov stability was verified. While the second phase involves the addition of a robust term and the use of an adaptive recursive design procedure to determine the controller parameters for pitch and yaw angles. The proposed control strategy is compared with other techniques such as classical sliding mode control, backstepping, and RGDI-SMC controls. The proposed strategy is also implemented in real-time to characterize its performance. On the basis of obtained results, the considered perturbations were effectively addressed by the augmentation of adaptation laws and recursive control design. [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]

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]

A state–space integer–order approximation of commensurate–order systems is obtained using a data–driven interpolation approach based on Loewner matrices. Precisely, given the values of the original fractional–order transfer function at a number of generalised frequencies, a descriptor–form state–space model matching these frequency response values is constructed from a suitable Loewner matrix pencil, as already suggested for the reduction of high–dimensional integer–order systems. Even if the stability of the resulting integer–order system cannot be guaranteed, such an approach is particularly suitable for approximating (infinite–dimensional) fractional–order systems because: (i) the order of the approximation is bounded by half the number of interpolation points, (ii) the procedure is more robust and simple than alternative approximation methods, and (iii) the procedure is fairly flexible and often leads to satisfactory results, as shown by some examples discussed at the end of the article. Clearly, the approximation depends on the location, spacing and number of the generalised interpolation frequencies but there is no particular reason to choose the interpolation frequencies on the imaginary axis, which is a natural choice in integer–order model reduction, since this axis does not correspond to the stability boundary of the original fractional–order system. [ABSTRACT FROM AUTHOR]

This work presents the controlling of the pitch orientation of a twin-rotor multi-input multi-output system (TRMS) using the sliding mode control (SMC) technique with variable gains. In this, the SMC is designed using variable gains where the pitch orientation error of the vertical dynamics of the TRMS system is intended to render the control law. The magnitude of the variable gains is related to the absolute value of the tracking error. Further, this technique minimised the chattering effect by ensuring the robustness property of the sliding modes. This design establishes a relationship between first and higher-order SMCs. The proposed controller is adapted from the twisting algorithm (TA) and conventional SMC. The control algorithm does not overestimate disturbances, resulting in a large reduction in control energy. Finally, a Lyapunov stability analysis is devised to demonstrate asymptotic stability. Simulations demonstrate the pitch orientation control and robustness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surface (RIS)-assisted cell-free (CF) massive Multiple-Input Multiple-Output (MIMO) technology exhibits significant potential in enhancing the energy efficiency of 6G mobile communications. Nevertheless, recent studies suggest that both access points (APs) and RISs encounter challenges related to a high energy consumption during operation. To address this issue, strategies involving AP hibernation and RIS shut-off are proposed. Subsequently, an optimization problem is formulated to jointly optimize RISs, beamforming vectors, and AP selection with the aim of maximizing the energy efficiency (EE). Initially, the non-convex optimization problem for maximizing energy efficiency is decomposed into three sub-problems. These sub-problems are subsequently reformulated using fractional programming and variational programming techniques and then solved using the successive convex approximation (SCA) algorithm, Dinkelbach algorithm, and greedy algorithm, respectively. Subsequently, an alternate optimization algorithm based on block gradient descent is introduced to iteratively solve the four-variable optimization problem, thereby obtaining an approximate solution to the original problem. The simulation results demonstrate that the algorithm significantly reduces energy consumption. Specifically, compared to the scheme without the hibernation strategy, the energy efficiency (EE) is enhanced by 35%. [ABSTRACT FROM AUTHOR]

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This article proposes a dual-band (2.4/5.8 GHz) wearable MIMO antenna system that can be applied to the ISM band. The antenna element uses an improved coplanar waveguide feeding structure. The four-element antenna system achieves high isolation between antenna elements through the self-decoupling structure of the antenna element and the orthogonal placement. The use of a floating ground structure effectively suppresses back radiation. Through the combination of coplanar waveguide feeding and floating ground, the antenna not only has a low profile but also improves the front-to-back ratio of antenna radiation, reducing the SAR value. The antenna also uses felt as a flexible dielectric substrate and conductive cloth as a radiation element, making the antenna flexible. [ABSTRACT FROM AUTHOR]

This paper introduces a novel approach aimed at enhancing the control performance of a specific class of unknown multiple-input and multiple-output nonlinear systems. The proposed method involves the utilization of a fractional-order fuzzy sliding mode controller, which is implemented through online fractional-order reinforcement learning (FOFSMC-FRL). First, the proposed approach leverages two Takagi–Sugeno–Kang (TSK) fuzzy neural network actors. These actors approximate both the equivalent and switch control parts of the sliding mode control. Additionally, a critic TSK fuzzy neural network is employed to approximate the value function of the reinforcement learning process. Second, the FOFSMC-FRL parameters undergo online adaptation using an innovative fractional-order Levenberg–Marquardt learning method. This adaptive mechanism allows the controller to continuously update its parameters based on the system's behavior, optimizing its control strategy accordingly. Third, the stability and convergence of the proposed approach are rigorously examined using Lyapunov theorem. Notably, the proposed structure offers several key advantages as it does not depend on knowledge of the system dynamics, uncertainty bounds, or disturbance characteristics. Moreover, the chattering phenomenon, often associated with sliding mode control, is effectively eliminated without compromising the system's robustness. Finally, a comparative simulation study is conducted to demonstrate the feasibility and superiority of the proposed method over other control methods. Through this comparison, the effectiveness and performance advantages of the approach are validated. [ABSTRACT FROM AUTHOR]

Terahertz (THz) communication is becoming a key technology for future 6G wireless networks because of its ultra-wide band. However, the implementation of THz communication systems confronts formidable challenges, notably beam splitting effects and high computational complexity associated with them. Our primary objective is to design a hybrid precoder that minimizes the Euclidean distance from the fully digital precoder. The analog precoding part adopts the delay-phase alternating minimization (DP-AltMin) algorithm, which divides the analog precoder into phase shifters and time delayers. This effectively addresses the beam splitting effects within THz communication by incorporating time delays. The traditional digital precoding solution, however, needs matrix inversion in THz massive multiple-input multiple-output (MIMO) communication systems, resulting in significant computational complexity and complicating the design of the analog precoder. To address this issue, we exploit the characteristics of THz massive MIMO communication systems and construct the digital precoder as a product of scale factors and semi-unitary matrices. We utilize Schatten norm and Hölder's inequality to create semi-unitary matrices after initializing the scale factors depending on the power allocation. Finally, the analog precoder and digital precoder are alternately optimized to obtain the ultimate hybrid precoding scheme. Extensive numerical simulations have demonstrated that our proposed algorithm outperforms existing methods in mitigating the beam splitting issue, improving system performance, and exhibiting lower complexity. Furthermore, our approach exhibits a more favorable alignment with practical application requirements, underlying its practicality and efficiency. [ABSTRACT FROM AUTHOR]

This paper presents the performance evaluation of an amplify‐and‐forward (AF) relay‐assisted multiuser multiple input–multiple output (MU–MIMO) downlink transmission system for correlated fading channels. The overall system performance was improved by incorporating a double‐quadrature spatial modulation (DQSM) scheme. The bit error rate (BER) performance and detection complexity of the AF–MU–MIMO–DQSM system were analyzed and compared with those of a conventional AF–MU–MIMO system under the same conditions and parameters. The results showed that the correlated fading channel severely affected the performance of systems with higher spectral efficiency (SE). Considering an SE of 12 bpcu/user, the AF–MU–MIMO–DQSM system yielded a gain of up to 3 dB in BER performance compared with that of its conventional counterpart for the analyzed cases. In terms of detection complexity, the AF–MU–MIMO–DQSM system showed a reduction of up to 56 % compared with that of the conventional system for the optimal maximum likelihood detection criterion. [ABSTRACT FROM AUTHOR]

This article studies the adaptive tracking control for a type of multi-input multi-output nonlinear systems, which still is an open issue coming from considerations of system uncertainties, unmeasurable states, and asymmetric time-varying full-state constraints. Fuzzy logic systems (FLSs) are trained online to estimate and compensate for unknown nonlinear system dynamics, effectively improving uncertainties rejection capabilities. To solve unmeasurable states, a state observer, including FLSs, is built and does provide a fairly good observation accuracy. Notably, an asymmetric time-varying barrier Lyapunov function is constructed to realize full-state constraint satisfactions. Based on the above theoretical findings, an adaptive fuzzy tracker is designed in a recursive manner. A Lyapunov analysis demonstrates the stability of the closed-loop system, and particularly, the constrained conditions will not be violated during the whole operation. Simulation results show the usefulness of the developed tracker. [ABSTRACT FROM AUTHOR]

Summary: This article introduces a novel and groundbreaking approach combining multiple‐input‐multiple‐output (MIMO) technology with radio frequency (RF) energy harvesting. The proposed antenna consists of two semi‐circular monopole antenna components, optimized with dimensions of 89 × 51.02 × 1.6 mm3, that share a common ground plane to achieve MIMO characteristics. A series of split‐ring resonators on the ground plane significantly enhances the isolation between the two radiating components. Band‐notched features are performed in the 3.5 GHz WiMAX and 5.5 GHz WLAN bands through modified C‐shaped slots in the radiating patch and two rectangular split‐ring resonators serving as parasitic devices near the feed line. The reconfiguration of band‐notching is made possible by controlling the modes of the embedded PIN diodes. The two antenna elements maintain mutual coupling below −18 dB from 1.5–13 GHz, achieving an impressive 158.62% impedance bandwidth. The antenna's efficiency and gain experience significant drop, indicating effective interference suppression at the center frequencies of the notch bands, and its performance in MIMO systems is assessed through parameters including envelope correlation coefficient, port isolation, radiation patterns, efficiency, gain, and diversity gain. The simulated properties of the designed antenna closely align with the measured outcomes, demonstrating its reliability and consistency. Moreover, the article evaluates the antenna's potential for RF energy harvesting, achieving a maximum harvested energy of 4.88 V. This proposed antenna can be used in multiple applications, like wideband, band‐notching MIMO, and RF energy harvesting. This proposed antenna is an efficient, reconfigurable wideband MIMO antenna with novel RF energy harvesting capability. [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]

A reconfigurable transmission metasurfaces (RTMs) excited by a multiple‐input multiple‐output (MIMO)(RTMs‐MIMO) antenna is designed to implement multi‐domain metamaterial computational imaging (MCI). Based on the receiver‐transmitter metasurfaces structure, four PIN diodes are applied to control the operating state of the RTMs. It enables arbitrary switching among four polarization transmission waves. A reverse‐induced current can be generated by adjusting the PIN diodes in the symmetrical position, resulting in a perfect 1‐bit phase modulation with a 3dB‐loss bandwidth of 15.38~19.89 GHz. A microstrip antenna with mushroom‐shaped parasitic patches is employed to implement the MIMO feeds. The ‐10dB bandwidth of MIMO antenna is compatible with the RTMs, and the maximum envelope correlation coefficient of 0.16 demonstrates good isolation between the MIMO feeds. In the MCI system constructed by the RTMs‐MIMO antennas, a transmission matrix H can be achieved by MIMO space beams, metasurfaces phase, metasurfaces polarization, and frequency modes. The correlation coefficient µg = 0.018 is achieved by the multi‐domain integration, allowing H to be highly non‐correlated. Based on the detection results of H, an imaging result with a relative reconstruction error of 0.372 is achieved. The designed MCI system therefore has unparalleled application prospects in the broad field of medical and military detection. [ABSTRACT FROM AUTHOR]

In this letter, a new learnable binary MIMO detection algorithm in massive MIMO systems with one‐bit analog‐to‐digital converters is proposed. A negative penalty is introduced to transform a non‐convex constraint to a convex box constraint and a learnable iterative algorithm is presented based on inexact alternating direction methods of multipliers. The proposed MIMO detection method performs better than the existing binary MIMO detection methods even with lower computational complexity. [ABSTRACT FROM AUTHOR]

In addition to stability, the system optimality has also received attention because the system is expected to achieve higher performance with lower energy consumption. In general, the conventional approach to achieve optimal control of nonlinear MIMO systems is to solve the Hamilton–Jacobi–Bellman equation directly, which is time-consuming and sometimes impossible. To address this issue, this paper proposes an adaptive neural inverse optimal control method for uncertain MIMO systems. The method is based on an improved design criterion for the inverse optimal controller, which avoids the need for constructing auxiliary systems and enables direct stability analysis of MIMO systems. Additionally, an adaptive one-parameter update strategy is proposed to reduce the computational effort, which avoids the need to update the entire neural network. The proposed scheme guarantees that the tracking errors of the MIMO system converge to a given domain while minimizing a family of meaningful loss functions. Finally, the effectiveness of the presented method is verified through simulations. [ABSTRACT FROM AUTHOR]

The accuracy of the channel state information is important for correct channel estimation. However, when conducting channel estimation, more resources are allocated to pilots for estimation compared to data transmission. Furthermore, when the number of users increases, the number of pilots for estimation increases. Subsequently, there is an increase in the transmission overhead and hence reduces the spectral efficiency. Therefore, the advantage of obtaining channel state information is significantly reduced. To improve the performance of massive MIMO systems, the study analyses the tradeoff between the number of resources required to correctly estimate the channel using pilots to avoid interference while maintaining optimum spectral efficiency in massive MIMO antennas. Therefore, this study proposes an algorithm to address the challenge of optimum resource allocation in a massive MIMO. Pilot Frequency reuse, max–min fairness algorithm, and Zadoff–Chu sequences were adopted to achieve optimal allocation of resources and reduce interference for users in different cells using the same frequencies. The results reveal improved performance in terms of spectral efficiency with the adoption of the resource optimization approach. The study contributes to the performance improvement of massive MIMO antennas for 5 G communications. [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]

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]

Due to increased complexity and interactions between various subsystems, higher-order MIMO systems present difficulties in terms of stability and control performance. This study effort provides a novel, all-encompassing method for creating a decentralized fractional-order control technique for higher-order systems. Given the greater number of variables that needed to be optimized for fractional order control in higher-order, multi-input, multi-output systems, the modified flower pollination optimization algorithm (MFPOA) optimization technique was chosen due to its rapid convergence speed and minimal computational effort. The goal of the design is to improve control performance. Maximum overshoot (Mp), rising time (tr), and settling time (ts) are the performance factors taken into consideration. The MFPOA approach is used to improve the settings of the proposed decentralized fractional-order proportional-integral-derivative (FOPID) controller. By exploring the parameter space and converging on the best controller settings, the MFPOA examines the parameter space and satisfies the imposed constraints by maintaining system stability. To evaluate the suggested approach, simulation studies on two systems are carried out. The results show that by decreasing the loop interactions between subsystems with improved stability, the decentralized control with the MFPOA-based FOPID controller provides better control performance. [ABSTRACT FROM AUTHOR]

A compact MIMO antenna has been designed explicitly for Ka-band for 5G wireless networks. The antenna uses four P-shaped radiating patches in E-shaped slots for polarization variety in the 20-40 GHz frequency band. The Rogers RT/duroid 5880 substrate (26 × 25 × 1.6) supports each patch and defective ground for each element. Polarization diversity lowers radiating element interference, making this antenna design advantageous. Therefore, the antenna has a wide bandwidth of 20 GHz. A high isolation level of over -20 dB has been attained by carefully examining the mutual connection between antenna radiators. After a detailed analysis of the radiation patterns, a gain of 17.6 dBi is found. Both total and radiating efficiency exceed 87% and 91%. MIMO system dependability is evaluated by assessing diversity characteristics including ECC, DG, CCL, MEG, and TARC, yielding findings of 0.0035, 9.98 dB, 0.013 bits/sec/Hz, -3 dB, and -10 dB. Simulations and observations show a significant agrement. [ABSTRACT FROM AUTHOR]

Mobile Ad Hoc Networks (MANETs) are dynamic and self-organizing networks where nodes constantly change positions and communicate wirelessly. The stability of routing protocols in MANETs is crucial for effective data transmission, considering factors like node mobility, bandwidth, and signal strength. This research introduces an innovative approach to enhance MANET routing stability by integrating optoelectronic devices and machine learning. Optoelectronic components are leveraged to optimize signal detection in 5G networks and beyond, particularly in Massive Multiple-Input, Multiple-Output (MIMO) systems. The proposed protocol, "Optoelectronic-Aided Machine Learning-Based Stable Routing Protocol," utilizes machine learning algorithms to intelligently select routes based on real-time data, improving network efficiency. Moreover, the integration of optoelectronic devices enhances signal detection and quality. Comprehensive evaluations were conducted to validate the effectiveness of this approach, comparing it with conventional routing algorithms such as AODV. The Network Simulator 2 evaluated key performance indicators such as round-trip latency, packet delivery ratio, and route lifetime. Specifically for 5G networks and Massive MIMO systems, the results show that this Optoelectronic-Aided Machine Learning-Based Stable Routing Protocol has the potential to improve the stability and efficiency of MANETs considerably. This study aids in the development of cutting-edge wireless network communication protocols. [ABSTRACT FROM AUTHOR]

Millimeter wave massive multiple-input and multiple-output (MIMO) systems support several antennas at the receiver and transmitter sides to serve several users in a communication system. Due to its several antennas, the system cost is very high and faces challenges such as blockage, path loss, and interference. To solve the above interference problem, beamforming technology and LMS Kalman filter-based Hybrid Precoding are used in millimeter wave massive MIMO system by directing the signal in the desired direction and to get a reduced bit error rate. Therefore, proposed hybrid precoding aims to leverage the advantages of both digital and analog beamforming while significantly reducing the number of RF chains compared to the number of antennas, all while maintaining the benefits of spatial multiplexing. This paper compares the performance of various precoding techniques, including Analog-only Beamsteering, Zero Forcing (ZF), Minimum Mean Square Error (MMSE), Kalman, Minimum Square Error Fully digital (MSE Fully digital), and Proposed Least Mean Squares (LMS) Kalman Hybrid Precoding, using performance metric parameters such as spectral efficiency, energy efficiency, bit error rate, and Information rate. The LMS Kalman Hybrid Precoding improved the spectral efficiency by almost 6.228 bps/Hz at 10 dB with ten channel paths concerning the Analog-only Beamsteering and almost 3.748, 2.974, 2.758, and 0.399 bps/Hz for the Zero Forcing (ZF) Hybrid Precoding, MMSE, Kalman, and MSE Fully Digital Hybrid Precoding techniques, respectively, which shows the superiority of the Proposed Least Mean Squares (LMS) Kalman Hybrid Precoding. [ABSTRACT FROM AUTHOR]

Artificial neural networks (ANN), an Artificial Intelligence (AI) technique, are both bio-inspired and nature-inspired models that mimic the operations of the human brain and the central nervous system that is capable of learning. This paper is based on a system that optimizes the performance of an uncertain unmanned nonlinear Multi-Input Multi-Output (MIMO) aerodynamic plant called Twin Rotor MIMO System (TRMS). The pitch and yaw angles which are challenging to control and optimize in practice, are being used as the input to the Nonlinear Auto-Regressive with eXogenous (NARX) model, and eventually trained. The training features use the Matlab Deep Learning Toolbox. The NARX structure has its core in the neural networks' architecture. Data is collected from the TRMS testbed which is used to train the network. ANN as a Hybrid intelligent control strategy of ANN in combination with Pattern Search and Genetic Algorithm, is then utilized to optimize the parameters of the neural networks. At the end it was validated, tested and the optimized system run in simulation and compared with other intelligent and conventional controllers, with the proposed controller outperforming them, giving a very fast-tracking control, stable and optimal performance that satisfactorily met all our design requirements. [ABSTRACT FROM AUTHOR]

According to research, the performance of the massive MIMO system is constrained by pilot contamination. Recent developments in massive MIMO provide a variety of methods for the reduction of pilot contamination with diverse performances. There is a trade-off between the complexity of the system, the time-frequency resources needed to estimate the channel, and the effectiveness of pilot contamination mitigation. Limited research exists on the analysis of the combined effect of the three parameters, despite the impact of the parameters on the performance of massive MIMO multi-cell networks. Therefore, in this research, an algorithm consisting of a combined adaptive linear precoding and pilot assignment methods was designed for the reduction of pilot contamination in the massive MIMO multi-cellular network. The Maximum ratio Transmission (MRT) and Zero Forcing (ZF) precoding schemes were incorporated in the algorithm to diminish interfering signals from other cells while amplifying the intended signal, to improve the BER at low computational complexity. Pilot assignment methods of frequency reuse and the Zadoff–Chu sequence facilitated the optimal allocation of resources and reduced interference to improve spectral efficiency. The pilot assignment method ensures an optimal number of pilots are assigned to guarantee the efficient use of resources while precoding schemes eliminate interference. The effectiveness of the proposed algorithm was evaluated based on the ability to reduce BER, increase spectral efficiency, and reduce computational complexity. The results show improved performance of the proposed algorithm, compared to the conventional MRT and ZF precoders. Accordingly, this study contributes to the investigation of the impact of integrating adaptive linear precoding and pilot assignment methods for pilot contamination mitigation in massive MIMO systems. [ABSTRACT FROM AUTHOR]

Energy efficiency (EE) is one of the most important challenges in fifth generation telecommunication systems. One of the ways to solve this challenge is to allocate the suitable power to users. The goal of the authors in this paper is to solve the challenge of EE in massive MIMO systems. The objective function in the EE optimization problem is a non-convex function and also has two constrained: maximum transmission power and minimum data rate. To convert the objective function into a convex function, maximum ratio transmission (MRT) precoding and the lower bound of the data rate are used. In order to obtain the lower bound of the data rate, the inequality of the lower bound is used. Lagrange dual function is also used to eliminate existing constrained. Since the power of the users is obtained by optimizing a lower bound, the successive convex approximation method (SCAM) can obtain a good result. According to this method, an iterative algorithm is introduced that solves the optimization problem numerically. One of the features of the proposed algorithm is that it has low computational complexity and can reach the optimal value faster than existing algorithms. The simulation results show that the proposed method performs better than the existing methods and has good results in the field of EE of massive MIMO systems. [ABSTRACT FROM AUTHOR]

In recent times, the advent of innovative technological paradigms like the Internet of Things has paved the way for numerous applications that enhance the quality of human life. A remarkable application of IoT that has emerged is the Internet of Vehicles (IoV), motivated by an unparalleled surge of connected vehicles on the roads. IoV has become an area of significant interest due to its potential in enhancing traffic safety as well as providing accurate routing information. The primary objective of IoV is to maintain strict latency standards while ensuring confidentiality and security. Given the high mobility and limited bandwidth, vehicles need to have rapid and frequent authentication. Securing Vehicle-to-Roadside unit (V2R) and Vehicle-to-Vehicle (V2V) communications in IoV is essential for preventing critical information leakage to an adversary or unauthenticated users. To address these challenges, this paper proposes a novel mutual authentication protocol which incorporates hardware-based security primitives, namely physically unclonable functions (PUFs) with Multi-Input Multi-Output (MIMO) physical layer communications. The protocol allows a V2V and V2R to mutually authenticate each other without the involvement of a trusted third-party (server). The protocol design effectively mitigates modeling attacks and impersonation attempts, where the accuracy of predicting the value of each PUF response bit does not exceed 54%, which is equivalent to a random guess. [ABSTRACT FROM AUTHOR]

Industry 4.0, with its focus on flexibility and customizability, is pushing in the direction of wireless communication in future smart factories, in particular, massive multiple-input-multiple-output (MIMO) and its future evolution of large intelligent surfaces (LIS), which provide more reliable channel quality than previous technologies. At the same time, network slicing in 5G and beyond systems provides easier management of different categories of users and traffic, and a better basis for providing quality of service, especially for demanding use cases such as industrial control. In previous works, we have presented solutions for scheduling industrial control traffic in LIS and massive MIMO systems. We now consider the case of dynamic slicing in the radio access network, where we need to not only meet the stringent latency and reliability requirements of industrial control traffic, but also minimize the radio resources occupied by the network slice serving the control traffic, ensuring resources are available for lower-priority traffic slices. In this paper, we provide mixed-integer programming optimization formulations for radio resource usage minimization for dynamic network slicing. We tested our formulations in numerical experiments with varying traffic profiles and numbers of nodes, up to a maximum of 32 nodes. For all problem instances tested, we were able to calculate an optimal schedule within 1 s, making our approach feasible for use in real deployment scenarios. [ABSTRACT FROM AUTHOR]

For future wireless communications, Cell-free Massive Multiple-Input Multiple-Output (CF-mMIMO) systems and Non-orthogonal Multiple Access (NOMA) schemes are considered potential candidates to meet the greater coverage and capacity demands. Nevertheless, a traditional CF-mMIMO system faces scalability issues and poses numerous challenges in handling the expanding number of user equipment and ensuring their dependable connectivity, particularly in larger geographical areas. To address this challenge, a user-centric (UC) approach is implemented in a CF-mMIMO system, wherein a designated subset of access points (APs) serves a specific number of users from the entire pool of available APs. To implement a NOMA aided CF-mMIMO system, users must be grouped using a suitable clustering scheme to achieve greater spectral efficiency (SE), sum-rate, and reduced bit error rate (BER). For efficient user clustering, unsupervised machine learning (ML) algorithms, such as k-means, k-means++, and improved k-means++ are employed. In this paper, a multiuser NOMA aided CF-mMIMO system with a UC approach is investigated and closed-form expressions for intra-cluster interference and SINR are derived and the performance of the proposed system is analyzed in terms of achievable sum-rate and BER. The proposed system with the UC approach and three ML algorithms namely k-means, k-means++, and improved k-means++ demonstrate 12%, 10%, and 17% higher achievable sum-rate as compared to the NUC approach with same ML algorithms respectively. Similarly, the proposed system with UC and ML approaches exhibits 52%, 55% and 61% improved achievable sum-rate respectively, as compared to far pairing, random pairing, and close pairing schemes. Moreover, the system model is validated through the conformity of the theoretically derived bit error rate with the simulation results for a three-user scenario. [ABSTRACT FROM AUTHOR]