Single-input Single-output Research Articles

Efficient Channel Equalization and Performance Analysis for Uplink FBMC/OQAM-Based Massive MIMO Systems

For offset quadratic amplitude modulation filter bank multi-carrier (FBMC/OQAM) based massive multiple-input multiple-output (MIMO) systems, a response difference phenomenon in the process of designing multi-tap equalizers is founded in this paper, which degrades the equalization performance. To address this issue, we propose a multi-stage equalization based design scheme of multi-tap equalizers with low implementation complexity. The scheme can alleviate the impact brought by the phenomenon effectively and improve the equalization performance significantly. Besides, we analyze the signal-to-interference-plus-noise ratio (SINR) and ergodic achievable rate performance of the proposed scheme, and further elaborate its self-equalization property that inter-user interference and noise vanishes as the number of BS antennas increases. In this case, there only remains some residual interference within the same user, which is tiny enough to be ignored. Thus, SINR can approach the signal-to-interference ratio (SIR) upper bound, i.e., the SIR in single-input single-output (SISO) scenario when the channel response is unit impulse. Simulation results confirm the performance analysis and show that the proposed scheme with shorter equalizer length can outperform other schemes with longer equalizer length. Moreover, even with finite number of BS antennas, the interference brought by the channels can be almost eliminated.

Continuous Control Set Model Predictive Control of a Modular Multilevel Converter for Drive Applications

The Modular Multilevel Converter is a good alternative for high power, medium voltage drive applications due to its modularity and scalability. However, the control is complex and typically involves several highly-coupled circulating current feedback loops implemented using Single-Input Single-Output design tools. Additionally, each circulating current has many different frequency components and electrical sequences to increase the degrees of freedom in the controller. In this work, the use of Continuous Control Set Model Predictive Control is proposed in order to include cross-coupling and interactions between the state variables and consider the system constraints, such as maximum current and maximum output voltage. The controller is intended for drive applications and is designed to operate with good dynamic performance over the entire speed range of the machine. The control methodology proposed is experimentally validated using an 18-cell MMC prototype driving a cage machine. Additional experimental tests are performed using PLECS-RT HIL platforms.

Decentralized QFT Controller Design Based on the Equivalent Subsystems Method

Since the 1970s various decentralized control methodologies have been developed to deal with the challenge of controlling complex and/or spatially distributed systems with multiple inputs and multiple outputs (MIMO), e.g., chemical plants, power systems, water systems, etc. In general, the use of distributed information and control structures requires the synthesis of control laws in a constrained (decentralized) information structure. The article presents a novel frequency domain robust decentralized controller design method that is appropriate for uncertain dynamic MIMO systems with equal numbers of input and output variables, which consist of interconnected physical subsystems and are given as a set of square transfer function matrices. The main framework of the proposed method provides the Equivalent Subsystems Method (ESM), whereby the overall closed-loop system under a decentralized controller is stable if, and only if, all the individual closed-loop equivalent subsystems are stable. By generating equivalent subsystems for all transfer matrices, which describe the uncertain MIMO system, the individual uncertain equivalent subsystems are obtained as sets of respective frequency responses. Such representation allows the application of the QFT (quantitative feedback theory) approach to independently design local single-input single-output (SISO) robust controllers which constitute the resulting decentralized controller implemented in real subsystems. The designed controller ensures robust stability of the overall closed-loop system and the required performance as specified by the standard QFT performance specification types in both the equivalent subsystems and the overall closed-loop system. Compared to the existing method and references therein, the proposed method reduces the conservatism of the robust stability conditions and enables the exploitation of the benefits by the SISO QFT approach in the independent design of the robust decentralized controller. The developed design procedure is verified and illustrated in a case study on the robust decentralized level controller design of the quadruple tank process.

Roll Stabilisation of a Flexible Spacecraft using Quantitative Feedback Theory

Quantitative Feedback Theory (QFT) has achieved the status as a powerful design technique for the achievement of assigned performance over the specified range of plant uncertainties. This is due to the relative simple algebra and the transparency of the design methodology. A QFT solution to a Single Input Single Output (SISO) problem of roll stabilization of a flexible satellite is presented in this paper. A simple controller is designed which ensures robustness against parameter variations and disturbances. The results of the study, are compared with those obtained using an H* controller.

Experimental Comparison of Carrier Phase Recovery Algorithms for Uniform and Probabilistically Shaped QAM in a 324.1 Gb/S Fiber-mm-Wave Integration System at W-Band

We have experimentally implemented a photonics-aided large-capacity fiber-mm-wave wireless communication system employing a simple dual-polarized single-input single-output (SISO) wireless based on polarization multiplexing at the W-band. To compare the performance of different algorithms, 18G-baud, and 35G-baud 16-level quadrature-amplitude-modulation (16QAM), probabilistically shaped 16QAM (PS-16QAM), 64QAM and PS-64QAM signal using different carrier phase recovery (CPR) algorithms are transmitted in the system. Moreover, we compare the Viterbi–Viterbi (VV), improved new algorithm based on VV (NVV), blind phase search (BPS), and two-stage BPS algorithms’ computational complexity to better compare different algorithms. Using the experiment result, we can demonstrate that the BPS algorithm is about half a magnitude better than the NVV algorithm for PS-QAM signals, while the NVV algorithm has the lowest computational complexity. Additionally, we also achieve error-free wireless transmission at a net data rate of 324.1 Gb/s with the bit error ratio (BER) below the forward-error correction (FEC) threshold of 1 × 10−2 assuming soft-decision forward-error correction (SD-FEC) when using the BPS algorithm.

Data-Aided CSI Estimation Using Affine-Precoded Superimposed Pilots in Orthogonal Time Frequency Space Modulated MIMO Systems

An orthogonal affine-precoded superimposed pilot (AP-SIP)-based architecture is developed for the cyclic prefix (CP)-aided single input single output (SISO) and multiple input multiple output (MIMO) orthogonal time frequency space (OTFS) systems relying on arbitrary transmitter-receiver (Tx-Rx) pulse shaping. The data and pilot symbol matrices are affine-precoded and superimposed in the delay Doppler (DD)-domain followed by the development of an end-to-end DD-domain relationship for the input-output symbols. At the receiver, the decoupled pilot and data symbol are extracted by employing orthogonal precoder matrices, which eliminates the mutual interference. Furthermore, a novel pilot-aided Bayesian learning (PA-BL) technique is conceived for the channel state information (CSI) estimation of SISO OTFS systems based on the expectation-maximization (EM) technique. Subsequently, a data-aided Bayesian learning (DA-BL)-based joint CSI estimation and data detection technique is proposed, which beneficially harnesses the estimated data symbols for improved CSI estimation. In this scenario our sophisticated data detection rule also integrates the CSI uncertainty of channel estimation into our the linear minimum mean square error (LMMSE) detectors. The AP-SIP framework is also extended to MIMO OTFS systems, wherein the DD-domain input matrix is affine-precoded for each transmit antenna (TA). Then an EM algorithm-based PA-BL scheme is derived for simultaneous row-group sparse CSI estimation for this system, followed also by our data-aided DA-BL scheme that performs joint CSI estimation and data detection. Moreover, the Bayesian Cramer-Rao bounds (BCRBs) are also derived for both SISO as well as MIMO OTFS systems. Finally, simulation results are presented for characterizing the performance of the proposed CSI estimation techniques in a range of typical settings along with their bit error rate (BER) performance in comparison to an ideal system having perfect CSI.

Multiple-input multiple-output active vibration control of a composite sandwich beam by fractional order positive position feedback

Positive Position Feedback (PPF) is an active vibration control algorithm widely employed for the vibration mitigation of thin-walled structures equipped with active flexural patches. Fractional order PPF (FOPPF) is a recent development of PPF that has been shown to achieve substantially better spillover characteristics, thanks to the inclusion of fractional calculus. However, so far, FOPPF literature has focused almost exclusively on single-input–single-output (SISO) applications, with little consideration of multiple-input-multiple-output (MIMO) capabilities and no experimental work on the subject. To fill this gap, a MIMO FOPPF control architecture was tested numerically and experimentally on a cantilever composite sandwich beam of rectangular section. The feedback algorithm involved two piezoelectric actuators and two piezoelectric sensors in a collocated configuration for the control of the lowest four resonances of the beam, excited by a random external excitation. The participation matrices, necessary for the simulation of the response of the electromechanical system, were obtained with a recently developed experimental method. The fractional orders of derivation, together with other controller parameters such as gain, resonant frequency and damping ratio, were chosen according to an optimization algorithm aimed at the vibration amplitude reduction of the first four resonances of the controlled system. As a result, the performance of the vibration control resulted superior to the equivalent PPF with integer exponents, both in simulations and experiments.

Single and Multi-Point Non-Orthogonal Multiple Access based Power Adaptive Design for Improving Bit Error Ratio

Abstract In the framework of next-generation communication systems, Non-Orthogonal Multiple Access (NOMA) has attracted considerable interest. The fundamental advantage is that it has greater spectrum utilization than its orthogonal equivalents. This proposed work integrates Single-Input Single-Output NOMA (SISO) with Coordinated Multi-Point (CoMP). It uses both systems based on Quadrature Phase-Shift Keying (QPSK). A power-tolerant NOMA reduces the system’s vulnerability to erroneous power allocation by adaptively modifying each user’s signal power. The transmitted data is used to modify the power in the Power-Adaptive NOMA (PANOMA). PANOMA helps improve the Bit Error ratio and also improves the computational complexity. The Bit Error Rate (BER) and the lower limit capacity efficiency across Rayleigh fading channels are determined in precise closure representations of more than two consumer situations to measure its capability. The proposed method PA-CoMP-NOMA improves the Bit Error ratio in both systems. It improves the average BER among all users. Compared to its orthogonal cousin, NOMA has higher spectral efficiency. Nevertheless, our proposed method retains this feature as well as superior BER performance, although its spectral effectiveness is lower than that of the classic sum-rate based power NOMA.

Effect of MIMO scheme on MWIR FSO link & comparison with SWIR wavelength in the presence of different visibility and strong turbulence circumstances

Abstract In this article, the authors analyse the performance of the “multiple input multiple output (MIMO)” technique on a free space optical (FSO) communication link using the mid wavelength infrared (MWIR) and commonly used short wavelength infrared (SWIR) in the presence of various visibility conditions like clear weather, light haze, dense haze, fog, etc., and strong turbulence regime. A 5 km link range has been considered with different diversity MIMO schemes. The performance analysis has been made for both wavelengths in terms of bit error rate (BER) and link outage probability. As the MIMO diversity is increased, the performance has been improved compared to the single input single output (SISO) scheme. In the presence of foggy conditions and higher turbulence strength, the MWIR wavelength works properly, whereas the SWIR wavelength has not worked for the same signal to noise (SNR) ratio and the same link distance.

Attitude Control of Ornithopter Wing by Using a MIMO Active Disturbance Rejection Strategy.

This work proposes a mathematical solution for the attitude control problem of an ornithopter wing. An ornithopter is an artificial bird or insect-like aerial vehicle whose flight and lift movements are produced and maintained by flapping wings. The aerodynamical drag forces responsible for the flying movements are generated by the wing attitude and torques applied to its joints. This mechanical system represents a challenging problem because its dynamics consist of MIMO nonlinear equations with couplings in the input variables. For dealing with such a mathematical model, an Active Disturbance Rejection Control-based (ADRC) method is considered. The cited control technique has been studied for almost two decades and its main characteristics are the use of an extended state observer to estimate the nonmeasurable signals of the plant and a state-feedback control law in standard form fed by that observer. However, even today, the application of the basic methodology requires the exact knowledge of the plant's control gain which is difficult to measure in the case of systems with uncertain parameters. In addition, most of the related works apply the ADRC strategy to Single Input Single Output (SISO) plants. For MIMO systems, the control gain is represented by a square matrix of general entries but most of the reported works consider the simplified case of uncoupled inputs, in which a diagonal matrix is assumed. In this paper, an extension of the ADRC SISO strategy for MIMO systems is proposed. By adopting such a control methodology, the resulting closed-loop scheme exhibits some key advantages: (i) it is robust to parametric uncertainties; (ii) it can compensate for external disturbances and unmodeled dynamics; (iii) even for nonlinear plants, mathematical analysis using Laplace's approach can be always used; and (iv) it can deal with system's coupled input variables. A complete mathematical model for the dynamics of the ornithopter wing system is presented. The efficiency of the proposed control is analyzed mathematically, discussed, and illustrated via simulation results of its application in the attitude control of ornithopter wings.

Two-coordinate decomposed SISO models-based stability analysis of three-phase grid-connected inverter systems with asymmetric control

In this paper, the Single-Input Single-Output (SISO) theory-based stability analysis method for weak grids with a three-phase grid-connected inverter (GCI) incorporating asymmetrical control is proposed in dq-frame and sequence domain, respectively. Initially, the paper presents two-coordinate decomposed models for Multi-input Multi-output (MIMO) systems. These models reveal that the stability of the MIMO system can be evaluated by analyzing two open-loop gains in the dq-frame or a single gain in the sequence domain using Nyquist/Bode plots. Next, the proposed analytical method is compared with existing state-of-the-art techniques. Theoretical analysis demonstrates that the proposed method enables the separate design of asymmetrical control in the d-axis and q-axis using the dq-frame approach. Furthermore, it enables the identification of the system's instability axes, namely the d-axis and q-axis. On the other hand, the proposed method in the sequence domain offers a lower computational complexity while accurately identifying the system's frequency-coupled resonances. Finally, the effectiveness of the proposed method is validated through simulations and experiments, consolidating its practical applicability.

A low-complexity algorithm based on variational Bayesian inference for MIMO channel estimation

With an increase in the number of transmitters in multiple-input multiple-output (MIMO) communication systems, there is a cubic rise in the computational complexity of the traditional sparse Bayesian learning (SBL) channel estimation algorithm. While various algorithms are effective for single-input single-output (SISO) systems, they are not suitable for the MIMO scenario. This paper introduces a MIMO channel estimation algorithm based on variational Bayesian inference (VBI) by assuming the independence of the variational distribution among different channels. The high-dimensional channel vectors estimated in the conventional MIMO-SBL algorithm are decomposed into multiple parallel low-dimensional channel vectors with different sparsity using VBI. Consequently, the complexity exhibits a linear relationship with the number of transmitters, as demonstrated through numerical analysis. Simulations confirm the improved estimation accuracy of the MIMO-VBI algorithm. Experimental results reveal that MIMO systems can achieve lower bit error rates using the MIMO-VBI algorithm, with reduced runtime for channel lengths exceeding 100 symbols.

Hyperstability of Linear Feed-Forward Time-Invariant Systems Subject to Internal and External Point Delays and Impulsive Nonlinear Time-Varying Feedback Controls

This paper investigates the asymptotic hyperstability of a single-input–single-output closed-loop system whose controlled plant is time-invariant and possesses a strongly strictly positive real transfer function that is subject to internal and external point delays. There are, in general, two controls involved, namely, the internal one that stabilizes the system with linear state feedback independent of the delay sizes and the external one that belongs to an hyperstable class and satisfies a Popov’s-type time integral inequality. Such a class of hyperstable controllers under consideration combines, in general, a regular impulse-free part with an impulsive part.

Pilot Randomization to Protect MIMO Secret Key Generation Systems Against Injection Attacks

In this paper, we investigate the problem of secret key generation under an injection attack, which refers to tampering of pilot signals over the air so that part of the shared randomness observed at the legitimate parties is controlled by the adversary. It has been shown that to launch such an attack, an adversary only needs one extra antenna, compared to the legitimate parties, in a single input single output (SISO) network. In this work, we generalize this result for the multiple input multiple output (MIMO) case. Furthermore, we propose pilot randomization as a means to protect against injection attacks by reducing them to jamming attacks that constitute a less serious threat. Finally, we derive a closed-form expression for the secret key rate of the investigated MIMO setting.

MSTGAN: A Multi-Slot Conditional Generative Adversarial Network Based on Swin Transformer for Channel Estimation

Accurate channel estimation is the key to enhance the transmission performance of communication systems. In this letter, we propose a multi-slot conditional generative adversarial network (cGAN) based on Swin Transformer called MSTGAN for channel estimation in a single-input single-output (SISO) scenario. Specifically, the proposed MSTGAN could learn the temporal correlation feature from continuous channel slots by the 3D convolution and extract the deep feature well by the Swin Transformer to improve the accuracy of channel estimation. The model is trained with data augmentation to reduce the computational cost for offline training. The simulation results demonstrate that the proposed method outperforms the linear minimum mean square error (LMMSE) method and other deep learning methods. Furthermore, extension schemes of the MSTGAN to the multiple-input multiple-output (MIMO) case are also provided.

Prescribed-Time Control and Its Latest Developments

Prescribed-time (PT) control for nonlinear systems, originated from Song et al., has gained increasing attention among the control community. The salient feature of PT control lies in its ability to achieve system stability within a finite settling time user-assignable in advance irrespective of initial conditions. It is such a unique feature that has enticed many follow-up studies on this technically important area, motivating numerous research advancements. In this article, we provide a comprehensive survey on the recent developments in PT control. Through a concise introduction to the concept of PT control, and a unique taxonomy covering: 1) from robust PT control to adaptive PT control; 2) from PT control for single-input–single-output (SISO) systems to multi-input–multioutput (MIMO) systems; and 3) from PT control for an isolated system to multiagent systems, we present an accessible review of this interesting topic. We highlight key techniques, and fundamental assumptions adopted in various developments as well as some new design ideas. We also discuss several possible future research directions toward PT control.

MODELING OF MIMO SYSTEMS WITH UNIVERSAL CONTROLLER

Abstract. The control of multi-input multi-output (MIMO) systems poses significant challenges due to the intricate dynamics and interactions between multiple input and output channels. This paper presents a simulation study on MIMO systems control using the universal control technique. The proposed method offers a versatile framework for regulating MIMO systems with varying dynamics, utilizing a single control algorithm. The underlying concept of the universal control law is to devise a set of principles or strategies that can be universally applied to any system, regardless of its specific characteristics or parameters. To achieve this, the control algorithm is designed based on the derivatives balance of the system's input-output dynamics, granting the controller adaptability to both single-input single-output (SISO) and MIMO systems. Numerical examples are provided to demonstrate the effectiveness of the proposed method, involving a MIMO system comprising four inputs in a two-coupled distillation column process and two inputs in a twin rotor setup. The challenges associated with distillation column control, such as nonlinearity, disturbances, and interaction between multiple inputs and outputs, are considered. Twin rotor systems, consisting of two interconnected rotors, pose unique control difficulties, primarily arising from the coupling between the two rotors and their nonlinear behavior. The simulation results show the capability of the proposed universal controller to achieve precise and robust control over the MIMO system. Keywords: MIMO, Control, Simulation, Distillation Column Process, Twin Rotor.

Disturbance Observer-Based Terminal Sliding Mode Tracking Control for a Class of Nonlinear SISO Systems with Input Saturation

This paper focuses on the trajectory tracking control for general nonlinear single-input single-output (SISO) systems in which the output is not directly related to the control input. To address the tracking problem with the consideration of possible model uncertainty, external disturbance, and control input saturation, we employ the input-output feedback linearization technique and design a finite-time disturbance observer-based terminal sliding mode controller to improve the tracking performance and enhance the robustness. The stability analysis is carried out by using the Lyapunov method. To alleviate the chattering while achieving an acceptable control performance, a boundary layer method is adopted for the trade-off between the high-frequency control actions and the bounded unavoidable nonzero steady-state error. The proposed method is evaluated on the two typical nonlinear systems, which are fully linearizable and partially linearizable, respectively, and compared to the state-of-the-art method in terms of tracking and robustness through comprehensive numerical simulations. The results show that the proposed method not only renders the estimated disturbance error tends to be zero in finite time, but also has superiority in the fast reaction to disturbance and small tracking error without high-frequency chattering.

Forward Action to Stabilize multiple Time-Delays MIMO Systems

In this paper, the problem of closed-loop stability of linear time-invariant systems with multiple time-delays is studied. We prove that stability of closed-loop configurations of such systems with unitary feedback can be guaranteed by means of a forward control action. The case of single-input single-output (SISO) systems is first considered, and then, an extension to the more general case of multi-input multi-output (MIMO) systems with multiple time-delays is dealt with. Numerical examples illustrating the theoretical results are also discussed.

Neural network-based iterative learning control for trajectory tracking of unknown SISO nonlinear systems

This paper proposes a neural network-based (NN-based) data-driven iterative learning control (ILC) algorithm for the tracking problem of nonlinear single-input single-output (SISO) discrete-time systems with unknown models and repetitive tasks. The control objective is to make the output of the system track the reference trajectory in each iteration process. Therefore, at each relative time during every iteration process, a generalized regression neural network (GRNN) is used as the estimator to solve the key parameters of the system, and a radial basis function neural network (RBFNN) is used as the controller to solve the control input. Compared with the traditional ILC algorithm, the two complex solving processes, i.e., dynamic linearization and criterion function minimization, are replaced and simplified into the iterative training of GRNNs and RBFNNs. The proposed algorithm is out-of-the-box and uses a point-to-point method to calculate the control input for each relative time of the system iteration, driving the tracking error of the system to approach zero. In addition, it is proved that the tracking error of the system under the proposed control algorithm is uniformly ultimately bounded. Finally, a numerical example shows the effectiveness and superiority of the control algorithm, and a path tracking experiment of an unmanned vehicle further verifies its practicability.