Multi-input Multi-output Research Articles

A convex optimization approach to multi-objective design of two-stage compensators for MIMO linear systems

The previous design of two-stage compensators with multi-objectives concerns the closed-loop stability, the low-sensitivity, and the satisfactory time-domain performance. However, the design only considers the single-input single-output system. This paper aims to present the design of two-stage compensators for multi-input multi-output (MIMO) linear systems which contain dynamical interaction between inputs and outputs. When Youla parameterization is applied to the MIMO linear system, the design criteria can be cast as a nonlinear optimization. In addition, the infinite dimension optimization problem is approximated to a finite dimension problem with Ritz approximation. The suitable form of design parameters is proposed to ensure the low sensitivity of the MIMO system. The two-stage compensator design is formulated as convex optimization which is efficiently solved using available solvers. Finally, the two-stage compensators are designed for Wood and Berry’s model of binary distillation column. The numerical results indicate that all design objectives of frequency domain and time domain can be achieved by the two-stage compensators.

Hybrid Beamforming and Relay Selection for End-to-End SNR Maximization in Single-User Multi-Relay MIMO Systems.

This paper proposes a novel hybrid beamforming and relay selection scheme for spectral efficiency maximization in a non-regenerative multi-relay multi-input multi-output (MIMO) system. The analog beamforming component in the radio-frequency (RF) domain must follow an element-wise constant modulus constraint, which makes the underlying design problem mathematically intractable and therefore, it is quite challenging to obtain the global optimal solution. To address this problem, phase-only precoding/combining matrices are derived by maximizing the end-to-end received signal-to-noise ratio (SNR) under transmit power constraint at the source and each relay node. This task is achieved by decomposing the original complicated optimization problem into two independent components. The first component designs the RF precoder/combiner at source and relay nodes by maximizing the received SNR at relay nodes. While the second component attempts to derive the analog precoder/combiner at relay nodes and destination by maximizing the received SNR at the destination. Digital baseband processing matrices are obtained by deriving the closed-form expression, which minimizes interference among different sub-channels. Finally, the relay selection is made by maximizing the overall SNR from the source to the destination. Computer simulations reveal that the performance of the proposed algorithm is close to its fully digital counterpart and approximately 6% higher than the specified relay-assisted hybrid beamforming techniques. Moreover, the proposed method achieves more than 15% higher performance in a sparse scattering environment when compared with the given relay selection techniques.

Model-Free Adaptive Control of Hydrometallurgy Cascade Gold Leaching Process with Input Constraints.

Hydrometallurgy technology can directly deal with low grade and complex materials, improve the comprehensive utilization rate of resources, and effectively adapt to the demand of low carbon and cleaner production. A series of cascade continuous stirred tank reactors are usually applied in the gold leaching industrial process. The equations of leaching process mechanism model are mainly composed of gold conservation, cyanide ion conservation, and kinetic reaction rate equations. The derivation of the theoretical model involves many unknown parameters and some ideal assumptions, which leads to difficulty and imprecision in establishing the accurate mechanism model of the leaching process. Imprecise mechanism models limit the application of model-based control algorithms in the leaching process. Due to the constraints and limitations of the input variables in the cascade leaching process, a novel model-free adaptive control algorithm based on compact form dynamic linearization with integration (ICFDL-MFAC) control factor is first constructed. The constraints between input variables is realized by setting the initial value of the input to the pseudo-gradient and the weight of the integral coefficient. The proposed pure data-driven ICFDL-MFAC algorithm has anti-integral saturation ability and can achieve faster control rate and higher control precision. This control strategy can effectively improve the utilization efficiency of sodium cyanide and reduce environmental pollution. The consistent stability of the proposed control algorithm is also analyzed and proved. Compared with the existing model-free control algorithms, the merit and practicability of the control algorithm are verified by the practical leaching industrial process test. The proposed model-free control strategy has advantages of strong adaptive ability, robustness, and practicability. The MFAC algorithm can also be easily applied to control the multi-input multi-output of other industrial processes.

Machine learning-based ethylene and carbon monoxide estimation, real-time optimization, and multivariable feedback control of an experimental electrochemical reactor

Electrochemical reduction of CO2 gas is a novel CO2 utilization technique that has the potential to mitigate the global climate crisis caused by anthropogenic CO2 emissions, and enable the large-scale storage of energy generated from renewable sources in the form of carbon-based chemicals and fuels. However, due to the complexity of the electrochemical reactions, the explicit first-principles models for CO2 reduction are not available yet, and there has been a limited effort to develop process modeling, optimization and control of CO2 electrochemical reactors. To this end, a rotating cylinder electrode (RCE) reactor has been constructed at UCLA to understand the mass transfer and reaction kinetics effects separately on the productivity. In the RCE reactor, the applied potential strongly influences the reaction energetics and the electrode rotation speed affects the hydrodynamic boundary layer and modifies the film mass transfer coefficient, which involves convective and diffusive transport. The present work aims to develop a multi-input multi-output (MIMO) control scheme for the RCE reactor that integrates techniques from artificial and recurrent neural network modeling, nonlinear optimization, and process controller design. Specifically, production rates of two products from the experimental reactor, ethylene and carbon monoxide, are controlled by manipulating two inputs, applied potential and catalyst rotation speed. Process dynamics and controllability are analyzed, a feedback control strategy is designed and the controllers are tuned accordingly. The experimental electrochemical cell is employed to gather data for process modeling and implement the multivariable control system. Finally, the experimental results are presented which demonstrate excellent closed-loop performance by the control system and regulation of the outputs at three different set-points including an economically-optimal set-point.

A New Algorithm on Automatic Trimming for Helicopter Rotor Aerodynamic Loads

In order to simulate the flight state of the helicopter effectively, it is necessary to trim the helicopter during the forward flight in a wind tunnel test. Previously, due to the lack of an internal-control closed loop in the test rig, the helicopter-wind-tunnel-test trimming was carried out manually, with low test efficiency, unstable data quality, and high labor intensity. With the continuous development of computer technology and automatic control technology, the helicopter-wind-tunnel-test trimming technology has been developing in the direction of automation and intelligence. The helicopter wind tunnel test automatic trimming system is a typical multi-input–multi-output (MIMO), strongly coupled, and complex nonlinear system, involving data acquisition and a processing system, rotor control system, tail-supported mechanism control system, wind-tunnel-speed pressure control system, and other subsystems, which is difficult to describe with an accurate mathematical model. Therefore, in order to meet the needs of a 3 m diameter rotor model aerodynamic performance evaluation and noise characteristics research wind tunnel test, an error feedback variable step automatic trimming algorithm is proposed based on the fuzzy-control principle to realize automatic trimming of aerodynamic loads of rotor model in the forward flight state. To verify the effectiveness and reliability of the trimming strategy, a series of wind tunnel tests on a 3 m diameter scaled rotor model of a helicopter were conducted in the FL-17 aeroacoustics wind tunnel of China Aerodynamics Research and Development Center (CARDC) based on the Φ3m tail-supported helicopter rotor model wind tunnel test rig. The wind tunnel test’s results show that the proposed automatic trimming algorithm has the characteristics of fast trimming speed and high efficiency, which can realize the automatic trimming of rotor model aerodynamic loads under different test states in the wind tunnel test effectively and reliably and greatly improve the intelligence level of helicopter wind tunnel test.

Joint Transmit Waveform and Reflection Design for RIS-Assisted MIMO Radar Systems

In this letter, we investigate to deploy multiple reconfigurable intelligent surfaces (RISs) in multi-input multi-output (MIMO) radar systems for achieving better clutter suppression and multi-target detection performance. In particular, the space-time transmit waveform and receive filter of the radar and the reflection coefficients of the RISs are jointly optimized to maximize the minimum radar signal-to-interference-plus-noise ratio (SINR) under the constant-modulus power constraint and the restrict of RISs. We develop an efficient alternating algorithm to solve the resulting complicated non-convex problem. Simulation studies illustrate the advantages of deploying RISs in MIMO radar systems and the effectiveness of the proposed algorithm.

Micro-Drone Ego-Velocity and Height Estimation in GPS-Denied Environments Using an FMCW MIMO Radar

In the context of autonomous navigation, the vehicle trajectory estimation and the detection of surrounding obstacles are two critical functionalities that must be robust to difficult environmental conditions (e.g., fog, dust, and snow) and the unavailability of infrastructure signals (e.g., GPS). With the advantage of remaining operable in low-visibility conditions, radar sensors are good candidates to detect obstacles in an autonomous navigation context. In this article, we show that radars can also be successfully used for real-time trajectory estimation. We address the case of an autonomous micro-drone intended for the exploration of piping networks and embedding a frequency-modulated continuous wave (FMCW) multi-input multioutput (MIMO) radar. We show that using a beamforming technique to virtually steer the radar field of view (FOV), we can simultaneously estimate the horizontal and vertical velocities of the drone as well as its height. These results are first validated through simulations based on experimental drone flight data and a radar simulator. Then, using an Infineon 77-GHz FMCW radar, we show, through real-world experiments, the high performance attainable with our solution.

Subcarrier Index Modulation for Spectral Efficient Frequency Division Multiplexing in Multi-Input Multi-Output Channels

This work presents spectral efficient frequency division multiplexing with subcarrier index modulation (SEFDM-SIM) for multi-input multi-output (MIMO) wireless networks. SEFDM employs non-orthogonal frequency division multiplexing to enhance the spectral efficiency (SE) at the expense of enhanced inter-carrier interference compared with traditional orthogonal frequency division multiplexing (OFDM). In SEFDM-SIM, the information is conveyed not only by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$M$</tex-math></inline-formula> -ary signal constellations, as in SEFDM, but also by the indices of the subcarriers, which are activated according to the incoming bitstream. SE and bit error rate (BER) are used as the performance measures. Both the analytic and simulation results show that the proposed MIMO-SEFDM-SIM outperforms the conventional MIMO-OFDM and MIMO-OFDM-SIM in SE, while achieving an improved BER than the MIMO-OFDM and MIMO-SEFDM systems.

Theoretical and Algorithmic Study of Inverses of Arbitrary High-Dimensional Multi-Input Multi-Output Linear-Time-Invariant Systems

Nowadays, systems need to be built and/or characterized to handle exceptional circumstances or adapt to a world itself more complex. A typical phenomenon can often be found that systems are required to accommodate high-dimensional inputs and outputs. Although the theories and methods for inverting a single-input single-output (SISO) linear-time-invariant (LTI) system have been well established, the generalized framework (consisting of theories and algorithms) for extending to arbitrary high-dimensional multi-input multi-output (MIMO) scenarios is still unsubstantial in the existing literature as this extension is far from trivial. In this work, we would like to develop such a new framework for governing the inversion of arbitrary high-dimensional discrete-time MIMO LTI systems, where any individual transfer function from a certain input to a certain output may have the infinite-impulse-response (IIR) characteristics. We propose two new inversion algorithms to invert the transfer-function tensors (TFTs) of arbitrary MIMO LTI systems. The pertinent computational complexities are also investigated for our proposed two TFT-inversion algorithms. The approximation of the inverse of an arbitrary TFT by a finite-impulse-response (FIR) TFT is studied and the corresponding approximation-error analysis is derived as well. Finally, numerical evaluations are presented to study the computational complexities with respect to different TFT dimensions and ranks.

Channel Reconstruction-Aided MUSIC Algorithms for Joint AoA&amp;AoD Estimation in MIMO Systems

This letter discusses the issue of joint angle-of-arrival (AoA) and angle-of-departure (AoD) estimation for multi-input multi-output (MIMO) arrays, so as to conceive a novel one-snapshot multiple signal classification (MUSIC)-derived algorithm based on channel reconstruction therein. Compared to conventional two-dimensional MUSIC (2D-MUSIC), not only does the proposed algorithm avoid the problem of matrix rank deficit, but it also brings a prominent complexity reduction yet achieving satisfactory accuracy. Simulation results verify the utility and advancement of our algorithm, especially in the scheme with high real-time requirements.

Virtual Hardware-in-the-Loop FMU Co-Simulation Based Digital Twins for Heating, Ventilation, and Air-Conditioning (HVAC) Systems

In this paper, a novel self-adaptive control method based on a digital twin is developed and investigated for a multi-input multi-output (MIMO) nonlinear system, which is a heating, ventilation, and air-conditioning system. For this purpose, hardware-in-loop (HIL) and software-in-loop (SIL) are integrated to develop the digital twin control concept in a straightforward manner. A nonlinear integral backstepping (NIB) model-free control technique is integrated with the HIL (implemented as a physical controller) and SIL (implemented as a virtual controller) controllers to control the HVAC system without the need for dynamic feature identification. The main goal is to design the virtual controller to minimize the distinction between system outputs in the SIL and HIL setups. For this purpose, Deep Reinforcement Learning (DRL) is applied to update the NIB controller coefficients of the virtual controller based on the measured data of the physical controller. Since the temperature and humidity of HVAC systems should be regulated, the NIB controllers in the HIL and SIL are designed by the DRL algorithm in a multi-objective scheme (MO). In particular, the simulations of the HIL and SIL environments are coupled by a new advanced tool: function mockup interface (FMI) standard. The Functional Mock-up Unit (FMU) is adopted into the FMI interface for data exchange. The extensive research of HIL and SIL controllers shows that the system outputs of the virtual controller are controlled exactly according to the physical controller.

A new full-duplex analog RoF transmission system for 5G/5G+ broadband mobile communication

Abstract A new cost-effective mobile fronthaul architecture has been proposed by designing promising analog radio over fiber (ARoF) transmission system compliant with the next generation ultra-dense wavelength division multiplexing passive optical network (UDWDM-PON). It is conceived for standalone 5G/5G+ mobile communication systems driven by broadband multi-input multi-output (MIMO) millimeter wave data signals using two high spectral efficient modulations. The feasibility of the proposed system was firstly proven in terms of spectral tracing supported by theoretical analysis that disclosed good agreement. Furthermore, the obtained simulation results regarding the system performance evaluation after 22 km bidirectional optical fiber links were revealed high reliable characteristics including error-free transmissions at low receiver sensitivities, and measured power penalties less than 1 dB. In addition, the recovered eye diagrams maintained wide openings with high-quality patterns.

Convolutional fuzzy neural network based symbol detection in MIMO NOMA systems

Abstract One of the most important tasks to be considered in wireless communication systems, especially in multi-carrier systems such as Multi-Input Multi Output Non-Orthogonal (MIMO-NOMA), is to correctly estimate the channel state information for coherent detection at the receiver. A hybrid deep learning model, called convolutional fuzzy deep neural networks, is proposed in this study for accurately estimating channel state information and detecting symbols in MIMO-NOMA systems. The performance of this proposal has been compared to traditional algorithms like Least Square Error- Successive Interference Cancelation (LS-SIC) and linear minimum mean square (LMMSE-SIC), as well as to other deep learning methods such as convolutional neural networks. With this proposed scheme, significantly less bit error rate is obtained in both Rician and Rayleigh channel environment compared to other algorithms. In addition to the high performance of this scheme, the fact that it does not need channel statistics is another important advantage.

Block-Based Kalman Channel Tracking for LEO Satellite Communication With Massive MIMO

Low Earth orbit (LEO) satellite communication (SATCOM) is one of the most promising systems to be incorporated in the future 5G and beyond wireless networks. The combination of massive multi-input multi-output (mMIMO) with LEO SATCOM can effectively enhance the communication capacity and improve the system throughput. However, the channel acquisition and tracking are challenging due to the severe Doppler shift and long propagation delay under the LEO SATCOM channel’s time-varying propagation environment. In this work, we propose a block-based (BB) Kalman channel tracking scheme for LEO SATCOM with mMIMO. Firstly the mMIMO LEO SATCOM channel model with Doppler shift and propagation delay is proposed. To reduce the tracking times in one data frame, the BB Kalman channel tracking scheme is proposed under LEO SATCOM environment. The optimal block length problem is also studied. Simulation results prove the feasibility of the proposed scheme and the block length selection criteria.

Reconfigurable intelligent surface passive beamforming enhancement using unsupervised learning

&lt;p&gt;&lt;span lang="EN-US"&gt;Reconfigurable intelligent surfaces (RIS) is a wireless technology that has the potential to improve cellular communication systems significantly. This paper considers enhancing the RIS beamforming in a RIS-aided multiuser multi-input multi-output (MIMO) system to enhance user throughput in cellular networks. The study offers an unsupervised/deep neural network (U/DNN) that simultaneously optimizes the intelligent surface beamforming with less complexity to overcome the non-convex sum-rate problem difficulty. The numerical outcomes comparing the suggested approach to the near-optimal iterative semi-definite programming strategy indicate that the proposed method retains most performance (more than 95% of optimal throughput value when the number of antennas is 4 and RIS’s elements are 30) while drastically reducing system computing complexity.&lt;/span&gt;&lt;/p&gt;

Joint Channel Estimation and Mixed-ADCs Allocation for Massive MIMO via Deep Learning

Millimeter wave (mmWave) multi-user massive multi-input multi-output (MIMO) is a promising technique for the next generation communication systems. However, the hardware cost and power consumption grow significantly as the number of radio frequency (RF) components increases, which hampers the deployment of practical massive MIMO systems. To address this issue and further facilitate the commercialization of massive MIMO, mixed analog-to-digital converters (ADCs) architecture has been considered, where parts of conventionally assumed full-resolution ADCs are replaced by one-bit ADCs. In this paper, we first propose a deep learning-based (DL) joint pilot design and channel estimation method for mixed-ADCs mmWave massive MIMO. Specifically, we devise a pilot design neural network whose weights directly represent the optimized pilots, and develop a Runge-Kutta model-driven densely connected network as the channel estimator. Instead of randomly assigning the mixed-ADCs, we then design a novel antenna selection network for mixed-ADCs allocation to further improve the channel estimation accuracy. Moreover, we adopt an autoencoder-inspired end-to-end architecture to jointly optimize the pilot design, channel estimation and mixed-ADCs allocation networks. Simulation results show that the proposed DL-based methods have advantages over the traditional channel estimators as well as the state-of-the-art networks.

Distributed RIS-assisted mmWave multi-user MIMO beamspace system

We are considering a novel distributed reconfigurable intelligent surfaces assisted (RIS-assisted) millimeter-wave (mmWave) multi-user multi-input multi-output (MIMO) beamspace system. Beamspace systems offer a low-scattering communication to users through beams. To enrich the propagation environment, multiple reconfigurable intelligent surfaces are installed on top of the surrounding buildings to provide an additional communication link, between the base station and users, by introducing phase shifts in the incoming signal arriving at it. Such systems allow the selection of two distinct beams for each user. The first beam is directly steered towards the user and the second beam is steered to the user via RIS. The selection of desirable beams is required to reduce the number of radio frequency (RF) chains at the transmitter. In this paper, we propose a beam selection algorithm that can assign two distinct and unshared beams to each user after optimally adjusting the phases of each RIS. Additionally, we propose a max–min fairness power allocation scheme to obtain a minimum achievable rate for each user. Simulation results compare the sum-rate performance with the existing beam selection algorithms.

Joint DOD and DOA detection for MIMO radar based on signal subspace reconstruction and matching

In this study, the Direction Of Departure (DOD) and Direction Of Arrival (DOA) of signals detection for Multi-Input Multi-Output (MIMO) radar is discussed. A novel signal subspace reconstruction model to match the signal subspace obtained based on the covariance matrix of the array output is developed to enhance the performance of the DOD and DOA detection. In the developed scheme, the technology of beamforming is first introduced to define an objective space in mathematics for the targets to be detected. By considering the orthogonality between the signal subspace and the noise subspace and defining a reconstruction index of the signal subspace, a multi-dimensional objective function of the DOD and DOA is established. Therefore, the problem of DOD and DOA detection is transformed into an optimization of the multi-dimensional objective function. Subsequently, the Quantum-Behaved Particle Swarm Optimization (QPSO) is employed to optimize the multi-dimensional objective function and to determine an optimal signal subspace. At the same time the DOD and DOA can be fast captured. A series of simulations demonstrate that the proposed method provides significant accuracy improvements in DOD and DOA detection, especially for low signal-to-noise ratio thresholds and small snapshots.

Low complexity symmetric-coded based sphere decoding for low-rate polar codes

The sphere decoding (SD) algorithm can provide (sub)optimal solutions with reduced computational complexity of maximum likelihood (ML) detection for multi-input multi-output (MIMO) communication systems. In this paper, we propose a novel low complexity symmetric-coded based SD algorithm for short polar codes with low rate. At the encoding stage, the first N/2 sub-channels transmit the frozen bits, while the information bits are selected from the latter N/2 sub-channels. Two symmetric codes are generated due to the mathematical structure of the generator matrix, which is well conditioned to the SD search. At the decoding stage, the presented SD algorithm computes the Euclidean distance value by the combined signals to estimate the latter N/2 input bits. Furthermore, the backtrack operation starts from the earlier (N/2+1)-th bit, which can significantly reduce the average visited nodes (AVN). Simulation results show that, compared to the original SD algorithm, the presented variant of the SD algorithm can reduce the AVN to 0.9% for the polar code P(64, 14) at SNR = 1 dB with a performance loss within 0.2 dB. The presented SD algorithm may find applications in MIMO systems where the complexity of the standar ML detection increases exponentially with the transmitting antennas.

Semi-supervised learning based hybrid beamforming under time-varying propagation environments

Hybrid precoding is considered as a promising low-cost technique for millimeter wave (mm-wave) massive Multi-Input Multi-Output (MIMO) systems. In this work, referring to the time-varying propagation circumstances, with semi-supervised Incremental Learning (IL), we propose an online hybrid beamforming scheme. Firstly, given the constraint of constant modulus on analog beamformer and combiner, we propose a new broad-network-based structure for the design model of hybrid beamforming. Compared with the existing network structure, the proposed network structure can achieve better transmission performance and lower complexity. Moreover, to enhance the efficiency of IL further, by combining the semi-supervised graph with IL, we propose a hybrid beamforming scheme based on chunk-by-chunk semi-supervised learning, where only few transmissions are required to calculate the label and all other unlabelled transmissions would also be put into a training data chunk. Unlike the existing single-by-single approach where transmissions during the model update are not taken into the consideration of model update, all transmissions, even the ones during the model update, would make contributions to model update in the proposed method. During the model update, the amount of unlabelled transmissions is very large and they also carry some information, the prediction performance can be enhanced to some extent by these unlabelled channel data. Simulation results demonstrate the spectral efficiency of the proposed method outperforms that of the existing single-by-single approach. Besides, we prove the general complexity of the proposed method is lower than that of the existing approach and give the condition under which its absolute complexity outperforms that of the existing approach.