Multiple-input Multiple-output Systems Research Articles

Robust sliding-mode observer for unbounded state nonlinear systems

Designing a full-state observer for nonlinear systems has always been accompanied by challenges and restrictive constraints. Mainly, applying a state observer in nonlinear systems with non-minimum phase characteristics is more challenging when the limiting constraints are not satisfied due to diverging internal dynamics. In this paper, a robust sliding-mode observer approach has been successfully employed to estimate the states of nonlinear systems with unbounded and diverging dynamics. The design principles of this observer are based on applying a classifying algorithm in single-input single-output and multiple-input multiple-output nonlinear systems. It is noteworthy that this observer is highly robust against disturbance, uncertainty and measurement noise, and its conditions are less conservative compared to previous nonlinear sliding-mode observers. One novel feature of the proposed observer is that while the system's state gets unbounded and diverged in fault-occurring scenarios or critical circumstances, this observer retains accuracy. The efficiency of the proposed observer is verified in the simulation results for two nonlinear industrial systems, including a hydro-turbine power generation plant and a continuous stirred tank reactor.

Pattern Diversity Based Four-Element Dual-band MIMO Patch Antenna for 5G mmWave Communication Networks

AbstractThis study presents a planar dual-band multiple-input multiple-output (MIMO) antenna design for the prospective fifth-generation (5G) frequency bands of 28 and 38 GHz. The antenna element is designed by utilizing a rectangular patch with an offset microstrip feeding technique. A dual-band response is achieved by placing semi-circular slots on each side of the patch element. To tune the frequency response and improve impedance matching, vertical rectangular slits are etched in the rectangular patch and the ground plane, respectively. The results show that the single antenna element offers an impedance bandwidth of 2.52 GHz (26.32–28.84 GHz) and 7.5 GHz (34–41.5 GHz). In addition, a MIMO configuration based on pattern diversity using four antenna elements is designed and fabricated. The designed MIMO configuration achieves an impedance bandwidth of 3 GHz (27–30 GHz) and 5.46 GHz (35.54–41 GHz) at operating bands of 28 and 38 GHz. The peak realized gain for the single element at 28 and 38 GHz is noted to be 7.4 dBi and 7.5 dBi, respectively. Furthermore, the polarization diversity configuration illustrates an isolation of > 15 dB and > 25 dB for the 28 and 38 GHz frequency bands, respectively. Moreover, the MIMO configuration attains appropriate values for the envelope correlation coefficient (ECC) and diversity gain (DG), Total Active Reflection Co-efficient (TARC), Channel Capacity Loss (CCL) and Mean Effective Gain (MEG) for the operating frequency bands. The proposed MIMO system based on results seems to be potential choice for mmwave Ka Band Applications.

Secure authentication in MIMO systems: exploring physical limits

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

Impact of memory on beamforming optimization for DDPG-assisted RIS-multi-user system

Recently, reconfigurable intelligent surfaces (RIS) have garnered considerable attention as indispensable components driving the evolution of future 6G wireless communication systems. This heightened interest is primarily attributed to notable advancements in programmable meta-material fabrication, which enable the creation of highly versatile and adaptable surfaces. These surfaces, often referred to as intelligent reflecting arrays, represent a significant departure from the conventional capabilities of massive multiple-input multiple-output (MIMO) systems, thereby catalyzing the emergence of intelligent radio environments characterized by enhanced flexibility and efficiency. Our study is dedicated to the comprehensive exploration of coordinated design strategies that encompass both the transmission beamforming matrix at the base station and the phase shift matrix at the RIS. Leveraging recent breakthroughs in deep reinforcement learning (DRL), our approach harnesses the power of a Long Short-Term Memory (LSTM) based DRL algorithm. This algorithm orchestrates the joint design process through iterative interactions with the environment, strategically guided by predefined rewards operating within a continuous framework of states and actions. While recent research has underscored the efficacy of LSTM-based architectures in bolstering the learning capacity of reinforcement learning (RL) algorithms and simplifying the search process, our investigation reveals a nuanced insight. Contrary to prevailing trends, we find that the integration of memory into the Deep Deterministic Policy Gradient (DDPG) with LSTM (DDPG-LSTM) algorithm yields unexpected consequences, negatively impacting the system's overall performance. Through preliminary simulation results, we empirically demonstrate the adverse effect of memory on DDPG performance when applied to RIS systems. This finding not only sheds light on the complexities of optimizing 6G wireless communication systems but also underscores the importance of careful algorithmic design and parameter tuning in achieving desired outcomes in emerging technologies.

Hybrid Multiple Access Techniques Performance Analysis Of Dynamic Resource Allocation

The Non-Orthogonal Multiple Access (NOMA) and the Orthogonal Frequency Division Multiple Access (OFDMA) are promising techniques for next-generation wireless systems. Although much attention in the literature considered these techniques, an effective combination between such systems as hybrid multiple access (HMA) needs more attention. For this reason, this paper devotes to proposing two different scenarios of HMA using multi-user OFDM (MU-OFDM) and power domain (PD) NOMA for downlink transmission to overcome the limitations of both Orthogonal Multiple Access (OMA) and NOMA. Due to the randomness of a wireless channel, different user grouping strategies and dynamic power allocation (DPA) strategies are employed to satisfy users’ requirements. The proposed systems give high flexibility utilizing bitrate allocation and user fairness. System results show superior performance to traditional OMA and NOMA systems. The achieved variety of fairness is helpful for the diversity of applications which is a principal requirement for beyond-fifth-generation (B5G) networks. The next step in this analysis is to enhance the proposed systems’ spectral efficiency (SE) by introducing beamforming for massive Multiple-Input Multiple-Output (MIMO) systems. The bit error rate (BER) result of the proposed system achieves almost the same error floor with a benefit of approximately 10 dB signalto-noise ratio (SNR) when 90 resource blocks (RBs) are used.  

Dual-Driven Learning-Based Multiple-Input Multiple-Output Signal Detection for Unmanned Aerial Vehicle Air-to-Ground Communications

Unmanned aerial vehicle (UAV) air-to-ground (AG) communication plays a critical role in the evolving space–air–ground integrated network of the upcoming sixth-generation cellular network (6G). The integration of massive multiple-input multiple-output (MIMO) systems has become essential for ensuring optimal performing communication technologies. This article presents a novel dual-driven learning-based network for millimeter-wave (mm-wave) massive MIMO symbol detection of UAV AG communications. Our main contribution is that the proposed approach combines a data-driven symbol-correction network with a model-driven orthogonal approximate message passing network (OAMP-Net). Through joint training, the dual-driven network reduces symbol detection errors propagated through each iteration of the model-driven OAMP-Net. The numerical results demonstrate the superiority of the dual-driven detector over the conventional minimum mean square error (MMSE), orthogonal approximate message passing (OAMP), and OAMP-Net detectors at various noise powers and channel estimation errors. The dual-driven MIMO detector exhibits a 2–3 dB lower signal-to-noise ratio (SNR) requirement compared to the MMSE and OAMP-Net detectors to achieve a bit error rate (BER) of 1×10−2 when the channel estimation error is −30 dB. Moreover, the dual-driven MIMO detector exhibits an increased tolerance to channel estimation errors by 2–3 dB to achieve a BER of 1×10−3.

Microwave Antenna Optimization for Low Latency and High Throughput Communication Systems

This paper presents a comprehensive exploration of microwave antenna optimization strategies aimed at achieving low latency and high throughput in modern communication systems. With the escalating demand for real-time applications and data-intensive services, the optimization of microwave antennas has become imperative to meet the stringent requirements of responsiveness and efficiency. The abstract delves into various facets of antenna optimization, including design parameters, signal processing techniques, and deployment considerations, all geared towards minimizing latency and maximizing throughput. Through a thorough review of existing literature and practical insights from real-world deployments, this research aims to provide valuable guidance for engineers and researchers in designing and implementing optimized microwave antennas for next-generation communication networks. By examining the intricate interplay between antenna design, signal processing algorithms, and deployment strategies, this paper elucidates the challenges and opportunities in achieving low latency and high throughput in microwave communication systems. Furthermore, it highlights the significance of factors such as gain, beamwidth, polarization, frequency band selection, multiple-input multiple-output (MIMO) systems, beamforming, adaptive coding and modulation (ACM), antenna placement, tower height, and line-of-sight considerations in optimizing antenna performance. The findings presented in this paper contribute to advancing the understanding of microwave antenna optimization and offer practical insights into enhancing the responsiveness, efficiency, and reliability of modern communication networks.

Product manifold optimization-based joint beamforming for RIS-aided MIMO systems: A fairness based approach

Reconfigurable intelligent Surface (RIS) can effectively improve the communication quality and extend the signal coverage range by reconfiguring the wireless communication transmission environment, which is very suitable for millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems. In this paper, a product manifold optimization-based beamforming design (PMO-BD) algorithm for a RIS-assisted multi-user MIMO system is proposed to enhance fairness among users. We establish a lower bound for the minimum signal-to-interference-plus-noise ratio (SINR) among users as the optimization objective, which is subjected to multiple constraints. By maximizing the lower bound to ensure the quality of service (QoS) for each user, fairness among users get improved. The PMO-BD algorithm exploits the internal structure of the product manifold to deal with the high dimension and nonlinear characteristics of the problem effectively, thereby reducing algorithm complexity and enhancing convergence rates. Simulation results verify the advantages of the proposed PMO-BD algorithm in comparison to other algorithms.

Data-driven calibration of decoupling matrix for MIMO precision motion stages

Decoupling control is a commonly employed technique for achieving high precision in multiple-input multiple-output (MIMO) motion control systems. A static decoupling matrix, which can be determined using geometric construction, is widely used due to its practicality and simplicity. However, inaccurate geometric parameters will lead to a coarse decoupling matrix, and result in interactions among the system axes and performance deterioration. To tackle this challenge, various attempts have been undertaken to calibrate the decoupling matrix. Data-driven on-line approaches have gained considerable attention for their ability to calibrate the decoupling matrix without interrupting the normal operation of the system. This paper presents a data-driven approach to calibrate the decoupling matrix for MIMO and linear time invariant (LTI) systems. Through some reasonable assumptions, a calibrated static decoupling matrix can be derived to improve the performance of the system. Moreover, considering the inevitable presence of measurement noise, the consistency of the proposed method has been analyzed. As a result, the instrument variable is introduced in the improved method to eliminate the impact of the measurement noise. Finally, the effectiveness and practicality of the proposed method are demonstrated through both numerical simulations and experiments carried out on an ultraprecision wafer stage.

SEAttention-residual based channel estimation for mmWave massive MIMO systems in IoV scenarios

To improve the accuracy and efficiency of time-varying channels estimation algorithms for millimeter Wave (mmWave) massive Multiple-Input Multiple-Output (MIMO) systems in Internet of Vehicles (IoV) scenarios, the paper proposes a deep learning (DL) algorithm, Squeeze-and-Excitation Attention Residual Network (SEARNet), which integrates Squeeze-and-Excitation Attention (SEAttention) mechanism and residual module. Specifically, SEARNet considers the channel information as an image matrix, and embeds a SEAttention module in residual module to construct the SEAttention-Residual block. Through a data-driven approach, SEARNet can effectively extract key information from the channel matrix using the SEAttention mechanism, thereby reducing noise interference and estimating the channel in an accurate and efficient manner. The simulation results show that compared to two traditional and two DL channel estimation algorithms, the proposed SEARNet can achieve a maximum reduction in normalized mean square error (NMSE) of 97.66% and 84.49% at SNR of -10 dB, 78.18% at SNR of 5 dB, and 43.51% at SNR of 10 dB, respectively.

Wireless powered NOMA-based cognitive radio for 6G networks

The need for physically charging mobile devices is anticipated to become a thing of the past in the not-too-distant future. In this paper, a novel approach for wireless-powered mobile devices is presented, which is based on non-orthogonal multiple access (NOMA) and multi-user multiple-input multiple-output (MU-MIMO) antenna systems. The proposed method is specifically designed for use in a cognitive underlay radio (CR) scenario, where the primary network requires a certain quality of service (QoS). Meanwhile, secondary network users can download their data using the same spectrum. The main objective of this paper is to maximize the sum rate of the secondary network. We have optimized a joint beamforming vector for both primary and secondary networks and a time-switching coefficient for energy harvesting and information transfer to achieve this objective. The initial problem formulation was non-convex, requiring the implementation of various techniques to ensure an efficient and low-complexity algorithm. To this end, we employed semi-definite programming, successive convex approximation, and alternating optimization. The proposed NOMA-based solution outperforms the TDMA-based benchmark scheme regarding sum data rate, specifically in the low transmit power region.

Mean squared error bound for learning-based multi-target localization and its application in learning network architecture design

The learning-based method has been widely applied in target localization. However, the performance of learning-based localization is mostly validated by simulation experiments and lacks theoretical analysis. In this paper, we investigate the performance of learning-based multi-target localization in a multiple-input multiple-output (MIMO) radar system with widely separated antennas. A mean squared error bound (MSEB) for learning-based multi-target localization is derived, which contains all the system parameters, including radar-related parameters and learning network-related parameters. Therefore, the MSEB can be employed as the metric to optimize any parameter of interest, such as those associated with the learning network architecture, which is usually determined with the trial-and-error method by minimizing the training loss. Leveraging the MSEB, we can design the optimal learning network architecture which maximizes the testing performance and this optimal architecture can be obtained before training. Numerical results are presented to verify the correctness of the MSEB and effectiveness of the MSEB-based learning network architecture design method.

Enhancing rational approximation of wideband resonant MIMO systems with frequency-domain data

Vector Fitting (VF) is a successful methodology for both time and frequency domain estimation of dynamic systems. These algorithms have gained popularity as a system identification tool due to their ability to provide a practical procedure for accurately approximating the measured frequency response behavior of complex and highly resonant dynamic systems using rational models. Instrumental Variable (IV) techniques can also be added to the VF algorithm to enhance the optimality of the solutions in the presence of measurement noise. In this context, this paper presents a multiple-input multiple-output (MIMO) generalization of instrumental variable (IV-)VF. The proposed algorithm can be applied to accurately estimating models formed by either continuous- or discrete-time rational basis functions (RBFs). We show that such a generalization preserves the main implementation features observed in Standard VF, but with improved optimality property for its solutions. Additionally, in order to alleviate convergence problems that may arise from the fixed high-frequency normalization, we also present in this paper how to incorporate in the proposed IV-VF method the relaxed formulation found in standard VF for both the continuous and discrete-time cases. The resulting MIMO IV-VF algorithm can be easily understood by VF users, and its applicability is discussed by using an estimation case study with two power system pieces of equipment (an 88 kV Transformer and a 550 kV Instrument Transformer) for which actual frequency domain measurements were collected in the field. It is shown that the proposed algorithm significantly improves the modeling accuracy when compared to standard VF algorithms.

Predicted position-driven deep learning channel estimation for massive MIMO systems

Multiple-input multiple-output (MIMO) can significantly improve the energy efficiency and spectral efficiency of wireless communication systems, and obtaining accurate channel state information is a key prerequisite. However, traditional channel estimation techniques often have high computational complexity, poor channel estimation performance, and do not fully utilize the information in the communication scene. In this paper, a channel estimator (PrePD-CNN) with convolutional neural network (CNN) driven by predicted position information is proposed based on high-precision localization technology for 6G. The channel estimation performance is improved by rationally transforming the sensing information and accurately reconstructing the quasi-channel state as auxiliary information. Simulation results show that the BER performance and MSE performance of PrePD-CNN are significantly lower than that of traditional channel estimation algorithms. Therefore, the proposed scheme combines stronger robustness and good generalization, which not only reduces the complexity of the algorithm but also improves the performance.

Latency and Residual Energy Analysis of MIMO Heterogeneous Wireless Sensor Networks

Energy-constrained Wireless Sensor Networks (WSNs) have garnered significant research interest in recent years. Multiple-Input Multiple-Output (MIMO), or Cooperative MIMO, represents a specialized application of MIMO technology within WSNs. This approach operates effectively, especially in challenging and resource-constrained environments. By facilitating collaboration among sensor nodes, Cooperative MIMO enhances reliability, coverage, and energy efficiency in WSN deployments. Consequently, MIMO finds application in diverse WSN scenarios, spanning environmental monitoring, industrial automation, and healthcare applications. This research paper presents a comparative performance analysis of MIMO wireless sensor networks and traditional wireless sensor networks without MIMO using Network Simulator NS2.35 for analysis of End to End Delay for packet transmission and Residual energy of nodes. The research work shows application of MIMO in Wireless Sensor Networks with considerable improvements in Quality of Service parameters which is achieved through Spatial Multiplexing and Diversity Gain. MIMO enables multiple spatial streams, allowing several data streams to be transmitted simultaneously on the same channel. This increases the overall throughput as multiple sensors can transmit their data concurrently without interference. MIMO systems also provide diversity gain by transmitting multiple copies of the same data over different antennas which helps in mitigating the effects of fading and interference, resulting in a more reliable and higher-throughput communication link as compared to a SISO channel. Another advantage of employing MIMO in WSN is reduction in End-to-End delays in data transmission. Last but not the least, MIMO can be configured to optimize the power consumption of individual sensors by adjusting the number of antennas used and transmission power levels based on channel conditions. Hence, MIMO can help to extend the network's lifetime by conserving energy in resource-constrained sensor nodes by preservation of Residual Energy.

Dynamic Decoupling Method Based on Motor Dynamic Compensation with Application for Precision Mechatronic Systems

Motors are widely employed in mechatronic systems, especially in precision multiple degrees of freedom motion systems. In most applications, the dynamic equation between the motor instruction and the actual driving force is simplified as a constant. Subsequently, the static decoupling method can be utilized to design the feedback controller. However, in high-precision mechatronic systems, motor dynamics cannot be neglected, and the static decoupling performance is compromised due to discrepancies between motors. In this paper, a dynamic decoupling method is developed to improve the decoupling performance of the multiple-input multiple-output systems. The effects of transmission delays, motor dynamics, and discrepancies between different motors are taken into consideration in the dynamic decoupling method. Furthermore, a data-driven optimization method is developed to estimate the parameters of the dynamic decoupling controller. The effectiveness and superiority of the proposed method are demonstrated through numerical simulations. The experimental results show that the dynamic decoupling control method can achieve a 97.75% performance improvement at least compared to the static decoupling control method.

A vector decomposition approach to F–M model realization and its application to robust observed-state feedback control

This work presents a novel approach based on vector decomposition for realizing the Fornasini–Marchesini (F–M) model in multidimensional (n-D) systems. The primary focus is on resolving coefficient dependencies within the n-D rational transfer function matrix (TFM) of a multiple-input multiple-output (MIMO) system, aiming to generate a lower-order F–M model. The proposed method is further extended to robust observational state feedback control. To achieve this, we introduce innovative sufficient conditions for designing a low-dimensional resolvent invariant space (RIS) and outline the corresponding low-order F–M model realization process. By incorporating the polynomial vector dependency technique, we comprehensively address coefficient dependencies, leading to the construction of an even lower-order F–M model and contributing to the simplification of analytical complexity. To accommodate generic rational TFMs, we propose a matrix fractional description (MFD)-based process. Finally, we apply the developed F–M model implementation method to derive descriptor multiple affine representations (DMARs). This application proves instrumental in the robust observed-state feedback control design for rationally uncertain systems, enhancing the overall effectiveness of the robust observed-state feedback control design.

A compact UWB MIMO antenna augmented with isolation improvement structures in situ with ground stubs and slots

Abstract An orthogonally oriented microstrip-fed bi-element ultra-wideband (UWB) diversity antenna possessing a super-wide bandwidth, high isolation, and band rejection attributes is proposed. The intended diversity antenna uses a 2nd-order Cayley fractal tree-shaped neutralization line among a pair of radiating square monopoles along with additional components like extended ground stubs, hybrid Koch fractal parasitic elements, and an L-shaped defected ground structure to attain high isolation of <−20 dB over 3.1–18 GHz. To nullify the intervention from the existent wireless local area network band, a hybrid Koch–Minkowski slot is carved out from the radiators. A minimal inter-element spacing of 8 mm is attained with the suggested layout measuring 28 mm (L) × 42 mm (W) in extent. The numerical as well as experimental investigations of vital diversity attributes like the envelope correlation coefficient, mean effective gain, total active reflection coefficient, and multiplexing efficiency depict high diversity actualization. The consistency amidst the simulation as well as the empirical results recommends the worthiness of the intended antenna for handy UWB and UWB multiple-input multiple-output systems.

Reinforcement Learning for Dual-Control Aircraft Six-Degree-of-Freedom Attitude Control with System Uncertainty

This article proposes a near-optimal control strategy based on reinforcement learning, which is applied to the six-degree-of-freedom (6-DoF) attitude control of dual-control aircraft. In order to solve the problem that the existing reinforcement learning is difficult to apply to the high-dimensional multiple-input multiple-output (MIMO) systems, the Long Short-Term Memory (LSTM) neural network is introduced to replace the polynomial network in the adaptive dynamic programming (ADP) technique. Meanwhile, based on the Lyapunov method, a novel online adaptive updating law of LSTM neural network weights is given, and the stability of the system is verified. In the simulation process, the algorithm proposed in this article is applied to the six-degree-of-freedom attitude control problem of dual-control aircraft with system uncertainty. The simulation results show that the algorithm can achieve near-optimal control.

Performance analysis and comparison of a novel Steiner-Quadrature Space Shift Keying (S-QSSK) scheme in massive MIMO systems

This paper introduces Steiner-quadrature space shift keying (QSSK) (S-QSSK), an innovative modulation scheme tailored for multiple–input multiple–output (MIMO) systems. The study compares S-QSSK with existing modulation schemes (S-space shift keying (SSK)(S-SSK), QSSK, generalized quadrature space shift keying (GQSSK)) across various performance metrics, considering diverse channel conditions, transmit antenna numbers, and spectral efficiencies. Theoretical average bit error rate (ABER) analysis, validated by Monte Carlo simulations, establishes a close agreement between analytical and practical results, especially at realistic signal-to-noise-ratio (SNR) values. Additionally, capacity and mutual information formulas shed light on the system’s information transmission capabilities. The research delves into the outage probability, power consumption, and computational complexity of S-QSSK MIMO systems. It unveils a promising trade-off, demonstrating that S-QSSK achieves competitive performance in ABER, capacity, and mutual information, positioning it as a practical modulation technique. Importantly, S-QSSK exhibits about a 1 dB ABER performance decrease compared to S-SSK and QSSK, while offering a noteworthy 2 dB improvement over GQSSK. Despite requiring slightly more transmit antennas than GQSSK, S-QSSK demands significantly fewer than both S-SSK and traditional QSSK, particularly when targeting high spectral efficiencies. Moreover, the analysis underscores the advantageous power consumption and favorable computational complexity of S-QSSK relative to other modulation schemes. The proposed S-QSSK MIMO system thus presents itself as an efficient solution, enabling high data rates with fewer transmit antennas, resulting in substantial reductions in power consumption and computational complexity without a significant sacrifice in ABER and mutual information. These attributes make it a compelling choice for various applications, including those requiring efficient massive multiple–input multiple–output (mMIMO) operations.