Multi-input Multi-output Systems Research Articles

Development of a Universal Adaptive Control Algorithm for an Unknown MIMO System Using Recursive Least Squares and Parameter Self-Tuning

Development of a Universal Adaptive Control Algorithm for an Unknown MIMO System Using Recursive Least Squares and Parameter Self-Tuning

Enhancing PAPR reduction efficiency in MIMO-OFDM systems via selective mapping and metaheuristic algorithms

The relentless evolution of communication systems, driven by the demands of 5G and the impending 6G networks, necessitates heightened data rates and spectral efficiency. orthogonal frequency division multiplexing (OFDM), a form of multicarrier modulation employed in multi-input multi-output (MIMO) systems, stands as a pivotal technology. Yet, OFDM grapples with challenges, notably the peak-to-average power ratio (PAPR) issue. Selective mapping (SLM) has been a favored technique for mitigating PAPR in OFDM, albeit challenged by computational complexities in its pursuit of discovering optimal phase factors. This paper pioneers a transformative approach by integrating metaheuristic algorithms genetic algorithm (GA), particle swarm optimization (PSO), and the innovative fireworks algorithm (FWA) into SLM for PAPR reduction while minimizing computational complexity. Simulation results not only affirm the efficacy of SLM-based techniques but also spotlight the potential of metaheuristic algorithms in addressing PAPR challenges in modern communication systems. The study transcends single-antenna systems, extending to MIMO-OFDM systems based on WiMAX standards, validating the efficacy of these techniques in multi-antenna configurations. Crucially, the FWA, proposed for the first time in this paper, emerges as a robust candidate, striking an enviable balance between computational efficiency and performance, achieving a notable PAPR reduction with a favorable search number.

Neuroadaptive Fault-Tolerant Control Embedded With Diversified Activating Functions With Application to Auto-Driving Vehicles Under Fading Actuation.

This article presents a neuroadaptive fault-tolerant control method for path tracking of multiinput multioutput (MIMO) systems in the presence of modeling uncertainties and external disturbances. In dealing with modeling uncertainties, neural networks (NNs) with diversified activation/basis functions are considered, with which we establish a set of control algorithms that are robust against uncertainties, adaptive to unknown parameters, and tolerant to actuation faults. This is the first work that explicitly takes into account the neural weights uncertainties and activating function uncertainties in multiple layered neural networks in control design. In addition, we apply the developed control algorithms to unmanned ground vehicles (UGVs) with actuator failures. With the aid of Lyapunov stability theory, it is shown that the proposed control is able to drive the vehicle along the desired path with high precision and all the internal signals are uniformly ultimately bounded (UUB) and continuous. Both theoretical analysis and numerical simulation confirm the effectiveness of the designed strategy.

Local DER Control with Reduced Loop Interactions in Active Distribution Networks

Active Distribution Networks are Multi-Input Multi-Output (MIMO) systems with coupled dynamics, which cause interactions among the control loops of Distributed Energy Resources (DERs). This undesired effect leads to performance degradation of voltage control. To mitigate the effects of this unavoidable coupling, the present paper proposes a systematic design procedure based on the analysis of the interaction’s sources. In detail, each DER is equipped with a double-loop PI to control the active and reactive power output of the voltage source converter, which connects the DER to the network’s node. Furthermore, to guarantee ancillary services, the two loops are coupled by a simple mechanism of cooperation of the active power to voltage regulation realized by a filtered droop law. To achieve voltage regulation with reduced loop interactions, the PI parameters and the filter’s pulse are designed according to a procedure with two sequential steps based on the Internal Model Control (IMC) technique. Simulation studies are finally presented to demonstrate that the proposed design method achieves both reduction of the loop interaction and robust voltage control in the presence of model parameter uncertainty in the MIMO plant, modeling various operating conditions of the ADN, including a step connection of large loads, renewable energy source variations, and changes in the substation transformer ratio.

Tensor z-Transform

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.

Design of MIMO PI-compensators without exponentially unstable poles

Abstract There is a class of systems that can only be stabilized by unstable compensators. However, for most practical systems suitable for use with linear regulators, this is possible with stable ones. To avoid exponentially unstable results in the design of pole placing PI-compensators it is of crucial importance to consider the position of the transmission zeros. This also has a favourable effect on the disturbance rejection. In multi-input multi-output (MIMO) systems, however, not only the zero position but also the zero direction is of importance. In this paper, a design method for multivariable PI-controllers in the time- and in the frequency-domains is presented which helps to avoid the occurrence of exponentially unstable controllers. A modified design method for multivariable PI-controllers, recently published in this journal, facilitates the procedure.

A dual‐wideband 5G antenna with dual polarization in the sub‐6 GHz band and 2 × 2 MIMO function in the 28‐GHz band

AbstractA 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.

Multi-loop active disturbance rejection control and PID control strategy for poultry house based on GA, PSO and GWO algorithms

Maintaining the health and welfare of broilers, besides obtaining and optimizing good performance, are the main objectives of poultry production. In response, climate control remains the most guaranteed strategy for managing livestock successfully. Separate controlling temperature and humidity on the one hand; and contaminant gases on the other was a focus of several investigations. Thus, the particularity of this work which involves the study, analysis, and control of broiler livestock building while taking into account, at the same time, all the system's constituent variables (i.e., temperature, humidity, NH3 and CO2 concentration, air velocity, and differential pressure). In this paper, an Active Disturbance Rejection Control (ADRC) and Proportional Integral Derivative (PID) controllers were designed and combined with a multi-loop approach for a multi-inputs multi-outputs (MIMO) system. Then, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO) were used to obtain the optimal controllers' parameters employing the reward function, the Integrated Time Absolute Error (ITAE), according to the poultry system requirements. Simulation experiments were carried out using the Matlab Simulink toolbox to verify the effectiveness of all the proposed control methods with the two optimization algorithms regarding stabilization and tracking setpoints. Despite the introduction of several disturbances in the plant model, the PSO-ADRC controller still exhibits notable benefits in terms of rise time, overshoot, settling time, and good disturbance rejection, proving the robustness of the suggested control method.

Improved Set-point Tracking Control of an Unmanned Aerodynamic MIMO System Using Hybrid Neural Networks

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.

A flexible mixed-optimization with H∞ control for coupled twin rotor MIMO system based on the method of inequality (MOI)- An experimental study.

This article introduces a cutting-edge H∞ model-based control method for uncertain Multi Input Multi Output (MIMO) systems, specifically focusing on UAVs, through a flexible mixed-optimization framework using the Method of Inequality (MOI). The proposed approach adaptively addresses crucial challenges such as unmodeled dynamics, noise interference, and parameter variations. Central to the design is a two-step controller development process. The first step involves Nonlinear Dynamic Inversion (NDI) and system decoupling for simplification, while the second step integrates H∞ control with MOI for optimal response tuning. This strategy is distinguished by its adaptability and focus on balancing robust stability and performance, effectively managing the intricate cross-coupling dynamics in UAV systems. The effectiveness of the proposed approach is validated through simulations conducted in MATLAB/Simulink environment. Results demonstrated the efficiency of the proposed robust control approach as evidenced by reduced steady-state error, diminished overshoot, and faster system response times, thus significantly outperforming traditional control methods.

MIMO–OFDM System Analysis and Achievement

Multi-Input–Multi-Output (MIMO) technology has already become crucial in modern communication study, but the needs of anti-interference capability and spectral efficiency are always growing, which poses a significant challenge in future development. In the paper, by analyzing and summarizing the respective characteristics and problems of MIMO system and Orthogonal Frequency Division Multiplexing (OFDM), we investigate the knowledge of MIMO–OFDM system. The theoretical analysis proves that this system can effectively solve the shortcomings of MIMO system and improve the performance of communication system. In addition, in the experimental simulation part, we also evaluate the system performance mainly by analyzing the Bit Error Rate (BER) metrics, while other methods such as channel estimation algorithms and synchronization techniques are also considered.

Mitigating mutual coupling effects on circular polarization for improved bandwidth in MIMO systems: A novel approach

An improved mutual coupling compensation in circularly polarized (CP) multi-input multi-output (MIMO) dielectric resonator antenna (DRA) is presented in this paper. Using trimming approach, the mutual coupling (MC) between closely spaced DRA units at 0.3λ has been significantly reduced while axial ratio performance has been maintained. Mutual coupling reduction is obtained by trimming the DRA to ensure low mutual coupling below −20dB. The exclusive features of the proposed MIMO DRA include wide impedance matching bandwidth (BW), triple band circular polarization, and suppressed MC between the radiating elements. The impedance bandwidth matches perfectly with a triple band's 3 dB axial ratio (AR). It is designed with characteristic mode analysis with good agreement of the measurement that has been obtained. Using the probe feed method, the DRA and patch strip are coupled together to allow bandwidth widening of the pro-posed DRA. An impedance bandwidth of 34% at a lower frequency to around 2% at a higher frequency was achieved in all resonance frequencies. Thus, we refer to our newly designed DRA as a proposed method for effectively reducing the mutual coupling between DRAs. Additionally, the 3 dB AR bandwidth matched at 3.3 GHz, 4.6 GHz, and 6.3 GHz with a percentage of 11.66%, 3.04%, and 2.22% obtained at the three different frequencies. Note that the proposed DRA exhibits low mutual coupling (below −20 dB) at the targeted frequencies, which is suitable for better signal reception for MIMO applications. By computing, the metrics envelop correlation coefficient, diversity gain, channel capacity loss, and total active reflection coefficient, the MIMO performance of the proposed antenna is verified. The experiments show a close result between simulated and computed validation of the proposed DRA.

Enhanced decentralised fractional-order control using tree seed optimisation and singular value analysis

Higher-order multi-input multi-output systems pose challenges in terms of stability and control performance due to increased complexity and interactions between multiple subsystems. Hence, this research work proposes a novel comprehensive approach to design a decentralised fractional order control strategy for higher-order systems using tree seed algorithm. The design focuses on enhancing control performance by imposing constraints on performance indices – the integral of absolute error and closed-loop amplitude ratio. Further, this work incorporates stability analysis through the singular values of the sensitivity function. The proposed decentralised fractional-order proportional-integral-derivative (FOPID) controller parameters are optimised using the tree seed algorithm. Besides, the stability is examined using the singular values of the sensitivity function. The tree seed algorithm explores the parameter space and converges to optimal controller settings that satisfy the imposed constraints by ensuring system stability. Simulation studies are conducted on various higher-order systems to analyse the proposed approach. The findings demonstrate that the decentralised control with tree seed algorithm-based FOPID controller achieves improved control performance by reducing the loop interactions among subsystems with enhanced stability. Furthermore, robustness is ensured by considering output uncertainty and parameter variations.

A new pilot assignment scheme for mitigating pilot contamination in uplink massive multi-input–multi-output (MIMO) systems

Pilot contamination is a serious issue in massive multi–input–multi–output systems which significantly degrades system performance. In this paper, we investigate a new pilot assignment scheme by integrating two-dimensional genetic algorithm with Tabu-Search algorithm (TS) to mitigate the pilot contamination problem. Firstly, we design a two-dimensional genetic algorithm equipped with elitism strategy as a principal algorithm for solving the pilot assignment problem; then, aiming to enhance the convergence speed of the genetic algorithm to the ideal optimal solution, we integrate TS with the genetic algorithm. This integrated pilot assignment scheme, henceforth designated as GATS-PA, is found to be powerful in mitigating the pilot contamination problem. Numerical simulation results verify that the proposed pilot assignment scheme is very close to the ideal optimal solution with few numbers of iterations and outperforms existing methods in terms of enhancing the average uplink rate per user over a wide range of simulation parameters.

Nonlinear Decoupling Control With PI λ D μ Neural Network for MIMO Systems.

In this brief, a fractional order proportional-integral-differential neural network (PIDNN) controller based on the beetle swarm optimization algorithm (BSO-PI λ D μ NN) is proposed for multi-input multi-output (MIMO) systems with strong coupling. First, the fractional order PID operator is introduced to the hidden layer neurons of the neural network, where long memory characteristics of the fractional order neurons can improve the control accuracy and convergence speed. Second, a sufficient condition on the learning rate is established to ensure the stability of the controller by the Lyapunov theory. Third, the PI λ D μ NN is initialized by the BSO algorithm to prevent weights from falling into local optima. The proposed fractional order PIDNN controller can eliminate the coupling between variables and achieve desirable control performance without specific system models. To the authors' best knowledge, this is the first work that the fractional order PI λ D μ neurons are employed in neural network. Two simulation examples verify the effectiveness and superiority of the proposed controller.

2D Beam Domain Statistical CSI Estimation for Massive MIMO Uplink

In this paper, we investigate the beam domain statistical channel state information (CSI) estimation for the two-dimensional (2D) beam-based statistical channel model (BSCM) in massive multi-input multi-output (MIMO) systems. The problem is to estimate the beam domain channel power matrices (BDCPMs) based on multiple received pilot signals. A received signal model showing the relation between the statistical properties of the received pilot signals and the BDCPMs is derived. On the basis of the received signal model, we formulate an optimization problem with the Kullback-Leibler (KL) divergence. By solving the optimization problem, a novel method to estimate the statistical CSI without the estimates of instantaneous CSI is proposed. We further reduce the complexity of the proposed method by utilizing the circulant structures of particular matrices in the algorithm. We also showed the generality of the proposed method by introducing another application, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., estimation of the angle domain channel power matrix. Simulation results show that the proposed method has good convergence and can obtain sparse BDCPMs. Compared with the regularized multiple measurement vector focal underdetermined system solver (RM-FOCUSS) algorithm, the proposed algorithm obtains overall more accurate statistical CSI with much lower complexity and brings significant performance gains when used in channel estimation.

Comprehensive review of isolation techniques in MIMO antennas for 5G mobile devices

Recently, due to the fast development in multimedia applications of wireless communication systems and the urgent demands of increasing the capacity and data rates to meet the requirements of 5G mobile communication systems, new transmission techniques are needed. One of the powerful enabling tools of the 5G communication systems is the Multi-Input Multi-Output (MIMO) antenna system employing multiple antennas and utilizing the multipath fading mobile environment to increase the channel capacity without any additional bandwidth and/or transmitted power. However, because of the limited space of the mobile devices, the distances between antennas are decreased. The mutual coupling between antennas severely affects the overall performance of the MIMO system. So, isolation improvement methods are applied to reduce these influences. In this review, the different isolation techniques and their features and drawbacks are presented, and the techniques for applying them in modern MIMO antenna systems are illustrated. Besides, the performance characteristics of these MIMO systems are compared. The review contains also the basic concepts of the MIMO antenna system and its performance metrics. Finally, the future directions and trends for the MIMO antenna system design are discussed briefly.

SE Analysis and GEE Optimization of Hardware-Impaired Multi-Cell Massive MIMO Systems With Rician Fading and Phase Shifts

We consider a practical hardware-impaired multi-cell massive multi-input multi-output (mMIMO) system with spatially-correlated Rician fading channels, whose line-of-sight component experiences a random phase shift. The multi-cell mMIMO system employs two-layer large-scale fading decoding (LSFD) to mitigate the interference due to pilot contamination. We consider a dynamic analog-to-digital converter (ADC) architecture at the base stations (BS), which enables us to independently vary the resolution of ADC connected to its each antenna, and thus properly choose them to achieve a high spectral efficiency (SE). Both BS and user equipments (UEs) are also equipped with low cost radio frequency chains, which introduce additional hardware impairments. We first derive a closed-form SE expression for this system, and then optimize the UEs uplink data power and LSFD vectors to maximize the global energy efficiency metric. The proposed optimization, which is practically implementable due to its closed-form updates, is derived by combining the quadratic and Dinkelbach transforms, and generalized Rayleigh quotient. We investigate the ADC resolution and hardware impairments values for which LSFD is highly effective, and the values for which its effectiveness reduces.

Performance enhancement of 5G uplink MIMO over fading-shadowing, path losses, and ISI using LZF equalize

The challenges of the 5G mobile wireless communication systems transmission channels have been changed continuously because of the signal transfer path characteristics such as real-time applications, path loss, multiple users, reflected rays, channel Rayleigh-fading, shadowing, noise, and inter symbol interference (ISI). These challenges have been treated to reach the minimum bit error rate (BER) value of the received data and maximum data rate of high-speed wireless mobile communication systems. In this paper, the linear zero-forcing (LZF) equalizer with uplink multi-input multi-output (MIMO) system has been proposed to enhancement the BER, which is caused by the ISI, Rayleigh fading, shadowing, path losses, and additive white gaussian noise (AWGN). Furthermore, the proposed approach is applied to a different number of antennas constellation to show the effect of increasing the number of antennas on the BER. The results show that the proposed LZF equalizer-MIMO system model has reached lower BER values, and high performance with an increase in the receiver to 8×14, 8×16 and 8×18 antennas constellation at 8, 10, 12, 14, 16, and 18 dB signal to noise ratio (SNR).

Two-Phase Non-Singular Terminal Sliding Mode Control of Nonlinear Systems

This paper presents a two-phase non-singular terminal sliding mode control scheme for a class of nonlinear systems in the presence of external disturbances. A two-phase terminal sliding mode surface is constructed and utilized, such that our control scheme achieves fast finite-duration convergence in regions both close to and far away from the control objective, by which the transition state is set and a less conservative bound of the settling time is obtained and superior to the existing results. Meanwhile, the singularity is avoided by the utilization of the saturation function, with the simplicity of structure and implementation. Extensions to multi-input multi-output systems are carried out further. The numerical simulation of the single inverted pendulum and the industrial manipulator are conducted to verify the effectiveness of the proposed control strategy.