Multi-input Multi-output Research Articles

Detection and performance analysis for MIMO visible light communication system using joint optical spatial and pulse amplitude width modulation

Conventional optical spatial modulation (SM) scheme activates one of the light-emitting diodes (LEDs) to transmit an intensity-modulated optical signal, in which the index of the activated LED is determined by spatial symbol and the emitted intensity is controlled by temporal symbol. In order to enhance the spectral efficiency (bits per channel use), we propose a joint SM and pulse amplitude width modulation (PAWM) as a novel optical spatial–temporal signaling scheme. In this paper, the proposed SM-PAWM optical signaling scheme is applied in a multi-input multi-output (MIMO) visible light communication (VLC) system. Employing optimal maximum likelihood (ML) algorithm to extract the spatial and temporal symbols is computationally prohibitive; hence, we develop a novel low-complexity detection scheme that converts the joint optimization problem separately to decode the spatial and temporal symbols. Moreover, theoretical results in terms of the successful identification probability of activated LED as well as the overall symbol error rate are derived. Extensive computer simulations are performed to validate the analytical results. It is shown that the proposed detection scheme is a feasible alternative to the ML detector in the VLC-MIMO system employing SM-PAWM.

A miniaturized-slotted planar MIMO antenna with switchable configuration for dual-/triple-band notches

This paper outlines the design and validation of a four-port compact Multi-Input Multi-Output (MIMO) antenna engineered to provide reconfigurable band notches within the ultra-wideband spectrum. The antenna features a circular monopole engraved with split ring slots along its radiating section, while a U-shaped slot is etched onto the feed line to facilitate band notches at specific frequencies—4.3 GHz (corresponding to INSAT), 6.8 GHz (aligned with Wi-Fi-6E), and 8.3 GHz (pertaining to X-band satellite). To ensure proper isolation, the MIMO elements are arranged orthogonally, and a decoupling periodic structure is strategically interposed between these elements to enhance isolation further. The incorporation of a PIN diode within the U-shaped slot introduces reconfigurability into the band notching mechanism. The MIMO antenna operates in two distinct modes depending upon the state of the PIN diode, either activated (ON) or deactivated (OFF). In the former mode, the antenna offers switchable band notches across three frequencies, while in the latter mode, it offers band notches at two specific frequencies. The validity of the antenna’s performance is validated through real-time fabrication and measurement. Both simulated and measured results exhibit a notable correlation. The antenna stands as an appropriate candidate for future cognitive radio applications.

Dual-band independently tunable 8-element MIMO antenna for 5G smartphones

In this paper, a multi-input multi-output (MIMO) antenna for future mobile phone applications operating in sub-6 GHza using a novel combination of an E-shaped slot placed on the ground plane and an F-shaped probe is used to achieve a dual-band independently tunable antenna. The proposed antenna is not only self-isolated but it has also effectively achieved high isolation of greater than 17 dB in both 3.5 GHz (3.4 – 3.6 GHz) and 4.7 GHz (4.6 – 4.87 GHz) frequency bands for 8-element MIMO antenna configuration for 5G smartphones. The simple yet compact size of (0.035 λ0▪ 0.23 λ0), of the slot antenna produces a balanced slot mode which not only reduces the ground effects but also improves the isolation between two adjacent input ports. The novelty of the proposed dual-band MIMO antenna is its independent control of each band across a wide frequency band and results demonstrate higher efficiency (64% – 71%) and diversity gain performance in both frequency bands. Furthermore, the antenna is designed by the meticulous configurations of 8-antenna elements without employing any external decoupling structure to attain the desired polarization diversity. The prototype of this 8-element MIMO antenna is also fabricated and measured to validate its simulated performance. The simple structure of the proposed design and high efficiency makes it a promising candidate for 5G smartphones.

Kinematics, dynamics and control of stiffness-tunable soft robots

Modeling and control methods for stiffness-tunable soft robots (STSRs) have received less attention compared to standard soft robots. A major challenge in controlling STSRs is their infinite degrees of freedom, similar to standard soft robots. In this paper, demonstrate a novel STSR by combing a soft-rigid hybrid spine-mimicking actuator with a stiffness-tunable module. Additionally, we introduce a new kinematic and dynamic modeling methodology for the proposed STSR. Based on the STSR characteristics, we model it as a series of PRP segments, each composed of two prismatic joints(P) and one revolute joint(R). This method is simpler, more generalizable, and more computationally efficient than existing approaches. We also design a multi-input multi-output (MIMO) controller that directly adjusts the pressure of the STSR’s three pneumatic chambers to precisely control its posture. Both the novel modeling methodology and MIMO control system are implemented and validated on the proposed STSR prototype.

Research of low-cost air quality monitoring models with different machine learning algorithms

Abstract. To improve the performance of the calibration model for the air quality monitoring, a low-cost multi-parameter air quality monitoring system (LCS) based on different machine learning algorithms is proposed. The LCS can measure particulate matter (PM2.5 and PM10) and gas pollutants (SO2, NO2, CO and O3) simultaneously. The multi-input multi-output (MIMO) prediction model is developed based on the original signals of the sensors, ambient temperature (T) and relative humidity (RH), and the measurements of the reference instrumentations. The performance of the different algorithms (RF, MLR, KNN, BP, GA–BP) with parameters such as determination coefficient R2, root mean square error (RMSE), and mean absolute error (MAE) are compared and discussed. Using these methods, the R2 of the algorithms (RF, MLR, KNN, BP, GA–BP) for the PM is in the range 0.68–0.99; the RMSE values of PM2.5 and PM10 are within 2.36–18.68 and 4.55–45.05 µg m−3, respectively; the MAE values of PM2.5 and PM10 are within 1.44–12.80 and 3.21–23.20 µg m−3, respectively. The R2 of the algorithms (RF, MLR, KNN, BP, GA–BP) for the gas pollutants (O3, CO and NO2) is within 0.70–0.99; the RMSE values for these pollutants are 4.05–17.79 µg m−3, 0.02–0.18 mg m−3, 2.88–14.54 µg m−3, respectively; the MAE values for these pollutants are 2.76–13.46 µg m−3, 0.02–0.19 mg m−3, 1.84–11.08 µg m−3, respectively. The R2 of the algorithms (RF, KNN, BP, GA–BP, except for MLR) for SO2 is within 0.27–0.97, the RMSE value is in the range 0.64–5.37 µg m−3, and the MAE value is in the range 0.39–4.24 µg m−3. These measurements are consistent with the national environmental protection standard requirement of China, and the LCS based on the machine learning algorithms can be used to predict the concentrations of PM and gas pollution.

Investigation of multi-input multi-output sine on random mixed vibration control

This article investigates Multi-Input Multi-Output (MIMO) mixed vibration control testing for Sinusoid on Random (SoR). The challenge of this vibration test is the mixed signal separation. The response signals at the control channels are composed of the broadband random component and the sinusoidal components. Firstly, the extracted accuracy of the Improved Correlation Integral Method (ICIM) is analyzed. The influences of the parameters used in ICIM are discussed in detail. Secondly, a new method, named Variance Minimum Method (VMM), is proposed for signal separation. Extraction accuracies of the ICIM and the proposed VMM are compared under different parameters. Finally, the control methods are proposed to iteratively update the driving signals of each component in the light of the deviations between the response signals and specified references. A cantilever beam example and biaxial vibration experiment manifest that the VMM and control method can be applied for the multi-shaker SoR vibration control tests.

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.

Quad-PortMIMO dielectric ResonatorAntenna with filtering response for IoT application

ABSTRACT This article presents a four-port Multi-Input Multi-Output (MIMO) antenna based on Dielectric Resonators (DR), showcasing excellent filtering characteristics, high isolation between ports, and strong diversity performance. The initial step introduces a single-port filtering Dielectric Resonator Antenna (DRA) utilizing the HEM11δ mode, featuring a complementary split ring resonator (SRR) feed structure that excites the cylindrical DR. This feed structure enables the independent adjustment of one resonance frequency and two radiation nulls. The experimental results confirm that the designed radiator works in between 2.35 and 2.57 GHz with an isolation level of more than 25 dB. The value of the gain is around 5.0 dBi within the operating band, while it is less than −15 dBi outside the working band. All these features make the designed MIMO radiator applicable for IoT applications.

A Soft Sensor for Multirate Quality Variables Based on MC-CNN.

In recent years, data-driven soft sensor modeling methods have been widely used in industrial production, chemistry, and biochemical. In industrial processes, the sampling rates of quality variables are always lower than those of process variables. Meanwhile, the sampling rates among quality variables are also different. However, few multi-input multi-output (MIMO) sensors take this temporal factor into consideration. To solve this problem, a deep-learning (DL) model based on a multitemporal channels convolutional neural network (MC-CNN) is proposed. In the MC-CNN, the network consists of two parts: the shared network used to extract the temporal feature and the parallel prediction network used to predict each quality variable. The modified BP algorithm makes the blank values generated at unsampled moments not participate in the backpropagation (BP) process during training. By predicting multiple quality variables of two industrial cases, the effectiveness of the proposed method is verified.

A Survey on User Grouping Schemes in Non- Orthogonal Multiple Access (NOMA)

In wireless networks, managing an increasing number of users becomes increasingly challenging as user density rises. To address these challenges, various algorithms are employed to divide users into smaller, more controllable groups. This approach is crucial for optimizing the performance of Non-Orthogonal Multiple Access (NOMA) systems, which are a key component of nextgeneration wireless networks like 5G and 6G. User grouping schemes in NOMA systems are essential for optimizing resource allocation, mitigating interference, improving spectral efficiency, ensuring fairness, and reducing complexity. As wireless networks evolve, particularly with the integration of advanced technologies like Coordinated Multi-Point (CoMP), Internet of Things (IoT), Millimeter- Wave (mmWave), Terahertz (THz), Unmanned Aerial Vehicles (UAVs), Multi-Input Multi-Output (MIMO), and 5G-6G, effective user grouping will become increasingly critical. This research paper provides a systematic classification and analysis of various user grouping schemes, offering valuable insights for enhancing network performance in next-generation wireless networks.

Robust control of electrohydraulic soft robots.

This article introduces a model-based robust control framework for electrohydraulic soft robots. The methods presented herein exploit linear system control theory as it applies to a nonlinear soft robotic system. We employ dynamic mode decomposition with control (DMDc) to create appropriate linear models from real-world measurements. We build on the theory by developing linear models in various operational regions of the system to result in a collection of linear plants used in uncertainty analysis. To complement the uncertainty analyses, we utilize ("H Infinity") synthesis techniques to determine an optimal controller to meet performance requirements for the nominal plant. Following this methodology, we demonstrate robust control over a multi-input multi-output (MIMO) hydraulically amplified self-healing electrostatic (HASEL)-actuated system. The simplifications in the proposed framework help address the inherent uncertainties and complexities of compliant robots, providing a flexible approach for real-time control of soft robotic systems in real-world applications.

Extracting operation rule of cascade reservoirs using a novel framework considering hydrometeorological spatiotemporal information based on artificial intelligence models

The scientific operation of cascade reservoirs is of great significance for completely utilizing water resources and maximizing their comprehensive benefits. The extraction of reservoir operation rules from massive historical operational data still remains a challenge. Therefore, a novel framework for extracting the operation rules of cascade reservoirs was proposed in this study based on artificial intelligence models by considering hydrometeorological spatiotemporal information. First, the observed precipitation was obtained by merging multi-source precipitation products through a convolutional long-short-term memory (ConvLSTM) network. Second, a novel extraction model by considering hydrometeorological spatiotemporal information was established, comprising a process of capturing the spatiotemporal information of interval precipitation using ConvLSTM and a feature aggregation process of aggregating precipitation and reservoir historical operation information using an LSTM network. Finally, three different extraction strategies (recursive, direct, and multi-input multi-output (MIMO) strategies) for cascade reservoir operation rules were developed and compared, and the influence of future multi-step forecast information was analyzed. Considering the example of Yalong River, the results are as follows: (1) the proposed extraction model has excellent fitting performance with a Nash-Sutcliffe model efficiency coefficient (NSE) of more than 0.95 and mean relative error (MRE) of less than 10 %, especially in reservoirs with large regulation capacity; (2) according to the regulation capacity of the reservoirs and reliability of forecast precipitation, appropriately adding multi-step forecast information can improve the performance of the extraction models; (3) each reservoir's NSE when using the three strategies is above 0.9, indicating that they can all be used to extract the operation rules of the cascade reservoir. Among them, the direct strategy is optimal.

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.

Efficient Ensemble via Rotation-Based Self- Supervised Learning Technique and Multi-Input Multi-Output Network

Efficient Ensemble via Rotation-Based Self- Supervised Learning Technique and Multi-Input Multi-Output Network

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.

Harmonic Suppression Strategy for Dual Three-phase Permanent Magnet Synchronous Machines Based on a Multi-Input Multi-Output Controller

Harmonic Suppression Strategy for Dual Three-phase Permanent Magnet Synchronous Machines Based on a Multi-Input Multi-Output Controller

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.

A Distributed Lightweight PUF-Based Mutual Authentication Protocol for IoV

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.

Command filter‐based finite‐time adaptive tracking control for MIMO dynamic systems with external disturbances and prescribed performance constraints

AbstractThis 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.

Energy optimization for full-duplex wireless-powered IoT networks using rotary-wing UAV with multiple antennas

In this paper, we propose a novel design for the rotary-wing unmanned aerial vehicle (UAV)-enabled full-duplex (FD) wireless-powered Internet of Things (IoT) networks. In this network, the UAV is equipped with an antenna array, and the K IoT sensors, which are distributed randomly, use single-antenna to communicate. By sending the energy, the UAV as a hybrid access point, charges the sensors and collects information from them. Then, to manage the time and optimize the energy, the sensors are divided into N groups, so that the UAV equipped with multi-input multi-output (MIMO) technology can serve the sensors in a group, during the total time T. We provide a simple implementation of the wireless power transfer protocol in the sensors by using the time division multiple access (TDMA) scheme to receive information from the users. In other words, the sensors of each group receive energy from the UAV, when it hovers over the sensors of the previous group, and also when the UAV flies over the previous group to the current group. The sensors of each group send their information to the UAV, when the UAV is hovering over their group. Under these assumptions, we formulate two optimization problems: a sum throughput maximization problem, and a total time minimization problem. Numerical results show that our proposed optimal network provides better performance than the existing networks. In fact, our novel design can serve more sensors at the cost of using more antennas compared to that of the conventional networks.