Multiple-input Multiple-output Systems Research Articles

Spatial Parameter Identification for MIMO Systems in the Presence of Non-Gaussian Interference

Reliable identification of spatial parameters for multiple-input multiple-output (MIMO) systems, such as the number of transmit antennas (NTA) and the direction of arrival (DOA), is a prerequisite for MIMO signal separation and detection. Most existing parameter estimation methods for MIMO systems only consider a single parameter in Gaussian noise. This paper develops a reliable identification scheme based on generalized multi-antenna time-frequency distribution (GMTFD) for MIMO systems with non-Gaussian interference and Gaussian noise. First, a new generalized correlation matrix is introduced to construct a generalized MTFD matrix. Then, the covariance matrix based on time-frequency distribution (CM-TF) is characterized by using the diagonal entries from the auto-source signal components and the non-diagonal entries from the cross-source signal components in the generalized MTFD matrix. Finally, by making use of the CM-TF, the Gerschgorin disk criterion is modified to estimate NTA, and the multiple signal classification (MUSIC) is exploited to estimate DOA for MIMO system. Simulation results indicate that the proposed scheme based on GMTFD has good robustness to non-Gaussian interference without prior information and that it can achieve high estimation accuracy and resolution at low and medium signal-to-noise ratios (SNRs).

Optimization of Signal Detection Using Deep CNN in Ultra-Massive MIMO

This paper addresses the evolving landscape of communication technology, emphasizing the pivotal role of 5G and the emerging 6G networks in accommodating the increasing demand for high-speed and accurate data transmission. We delve into the advancements in 5G technology, particularly the implementation of millimeter wave (mmWave) frequencies ranging from 30 to 300 GHz. These advancements are instrumental in enhancing applications requiring massive data transmission and reception, facilitated by massive MIMO (multiple input multiple output) systems. Looking towards the future, this paper forecasts the necessity for faster data transmission technologies, shifting the focus toward the development of 6G networks. These future networks are projected to employ ultra-massive MIMO systems in the terahertz band, operating within 0.1–10 THz frequency ranges. A significant part of our research is dedicated to exploring advanced signal detection techniques, helping to mitigate the impact of interference and improve accuracy in data transmission and enabling more efficient communication, even in environments with high levels of noise, and including zero forcing (ZF) and minimum mean square error (MMSE) methods, which form the cornerstone of our proposed approach. Additionally, signal detection contributes to the development of new communication technologies such as 5G and 6G, which require a high data transmission efficiency and rapid response speeds. The core contribution of this study lies in the application of deep learning to signal detection in ultra-massive MIMO systems, a critical component of 6G technology. We compare this approach with existing ELMx-based machine learning methods, focusing on algorithmic efficiency and computational performance. Our comparative analysis included the regularized extreme learning machine (RELM) and the outlier robust extreme learning machine (ORELM), juxtaposed with ZF and MMSE methods. Simulation results indicated the superiority of our convolutional neural network for signal detection (CNN-SD) over the traditional ELMx-based, ZF, and MMSE methods, particularly in terms of channel capacity and bit error rate. Furthermore, we demonstrate the computational efficiency and reduced complexity of the CNN-SD method, underscoring its suitability for future expansive MIMO systems.

A Compact Highly Isolated Four-Element Antenna System for Ultra-Wideband Applications

A small, orthogonally polarized, ultra-wideband (UWB), four-port multiple-input multiple-output (MIMO) printed antenna is presented in this study. The envisioned antenna is built up of four microstrip fractal-based circular patch elements, each of which is fed by a microstrip line with a 50-ohm impedance. The use of a defective ground plane allows for the ultra-wideband frequency response to be obtained. In order to achieve maximal isolation, the amount of surface current that flow between the antenna’s four components is limited by arranging radiating elements orthogonally. The four-port MIMO system is printed on a FR4 substrate with a loss tangent of 0.02 and an overall dimension of 20 × 30 × 1.6 mm3. A port-to-port isolation of less than 25 dB was achieved as a consequence of this orthogonal orientation of antenna elements, and the impedance bandwidth is achieved up to 158% (3.1–12 GHz). The suggested ultra-wideband multiple-input multiple-output (UWB-MIMO) antenna achieved a maximum gain of 8 dBi over the operational frequency range (3.1–12 GHz); the findings that were measured and those that were simulated accord with one another rather well. The findings also give an overall strong diversity performance, with the ECC < 0.25, DG > 9.9, and CCL < 0.2 values all being within acceptable ranges.

Adaptive fuzzy finite-time output feedback fault-tolerant control for MIMO stochastic nonlinear systems with non-affine nonlinear faults

This paper investigates adaptive fuzzy finite-time output feedback fault-tolerant tracking control problem for multiple-input multiple-output (MIMO) stochastic non-strict feedback nonlinear systems with non-affine nonlinear faults and states unmeasured. Firstly, high-gain fuzzy state observers are constructed to overcome the state unmeasured problem in the system. Secondly, the mean-value theorem and input compensation techniques are employed to decouple non-affine nonlinear faults, and the resulting unknown control coefficients problem is handled using Nussbaum functions. In addition, dynamic surface control techniques and error compensation mechanisms are considered in the controller design, which effectively decreases the computational complexity of the control scheme. By combining the adaptive backstepping technique with finite-time control, a novel adaptive fuzzy dynamic surface finite-time output feedback fault-tolerant tracking control algorithm is proposed. The designed controller guarantees that all signals in the stochastic systems are semi-global finite-time stable in probability (SGFSP) as well as the tracking errors can converge to a small neighborhood within the origin in finite-time. Finally, two simulation examples are introduced to verify the effectiveness of the suggested control strategy.

A 4-Element UWB MIMO Antenna System with High Isolation Performance

This paper proposes a 4-element multiple-input multiple-output (MIMO) antenna system, which is intended for ultra-wideband (UWB) applications. The antenna has a dimension of 92 × 70 × 1.6 mm3. It achieves element isolation via the defected ground structure (DGS) and symmetric E-shaped branch structures design. The realized operating bandwidth is from 3 GHz to 18 GHz, with 15 dB isolation within the whole wide band. The envelope correlation coefficient (ECC) is less than 0.01 with diversity gain (DG) greater than 9.95. The prototype is fabricated and measured to verify its potential applications for UWB MIMO communication.

Highly-efficient filtered hierarchical identification algorithms for multiple-input multiple-output systems with colored noises

Multiple-input multiple-output (MIMO) systems have extensive applications in industrial processes and systems engineering. This letter applies the filtering identification idea to establish a filtered identification model and investigate a filtered auxiliary model-based recursive least squares (F-AM-RLS) algorithm for parameter identification of MIMO systems with colored noises. To improve the computational efficiency, this work further proposes a four-stage filtered auxiliary model-based recursive least squares (4S-F-AM-RLS) algorithm by means of the hierarchical identification principle. Then, by incorporating the forgetting factor, a four-stage filtered auxiliary model-based forgetting factor recursive least squares (4S-F-AM-FF-RLS) algorithm is given to improve the convergence speed and the estimation accuracy. Additionally, the computational complexity analysis of the proposed algorithms indicates that the 4S-F-AM-RLS algorithm effectively reduces the computational burden and improves computational efficiency. Finally, the effectiveness of the F-AM-RLS, 4S-F-AM-RLS and 4S-F-AM-FF-RLS algorithms is validated through a numerical example.

Channel estimation for pilot contamination in massive MIMO-NOMA system

Channel estimation is crucial for massive multiple-input multiple-output (MIMO) systems to scale up multi-user (MU) MIMO, providing great improvement in spectral and energy efficiency. The nature of non-orthogonal cause pilot contamination is experienced only while estimating multi-cell MIMO scheme with the training and it is misplaced while narrowing concentration to multi-cell or one-cell setting, where information of the channel is assumed to be obtainable at no cost. Non-orthogonal multiple access (NOMA) serves numerous users concurrently utilizing channel gain differences. The advancement in massive MIMO-NOMA technology has offered diverse techniques recently for reducing pilot contamination in massive MIMO-NOMA based on pilot allocation. Here, a new approach called War Strategy Chimp Optimization+Deep Neuro-Fuzzy Network (WSChO+DNFN) is designed for the estimation of channels to reduce pilot contamination in a massive MIMO-NOMA system. It takes place in two phases, the transmitter and the receiver phase. The channel estimation is conducted by DNFN that is tuned by devised WSChO. Furthermore, WSChO is an amalgamation of War Strategy Optimization (WSO) and Chimp Optimization Algorithm (ChOA). Additionally, the WSChO+DNFN attained minimal values of BER and normalized MSE of 0.000103 and 0.000074, respectively. The proposed method has achieved a performance gain of 44.39%, 19.26%, 9.17%, 5.22%, 9.92%, and 6.03% compared to the Orthogonal Frequency Division Multiplexing (OFDM), Group Successive Interference Cancellation assisted Semi-Blind Channel Estimation Scheme (GSIC_SBCE), Sector-Based Pilot Assignment Scheme (PAS), Convolutional Neural Network (CNN), User Segregation based Channel Estimation (USCE), Optimal Channel Estimation using Hybrid Machine Learning (OCE_HML), respectively.

On autoregressive and neural methods for massive-MIMO channel de-noising

In modern wireless communication systems, the Multiple-Input Multiple-Output (MIMO) technology allows to greatly increase power efficiency, the serving area, and the overall cell throughput through the use of the antenna array beamforming. Nevertheless, the MIMO systems require accurate channel state knowledge to apply correct precoding. In 5G Time Division Duplex (TDD) systems, the Channel State Information (CSI) is obtained via Sounding Reference Signals (SRS) transmitted by the User Equipment (UE). UEs have limited power capabilities and thus cannot achieve high Uplink (UL) Signal-to-Noise Ratio (SNR) on gNodeB (gNB) in large bandwidth. There are multiple techniques that can be applied to improve the accuracy of Channel Estimation (CE) in noisy conditions. In this paper, we describe a classical method, namely the Vector Autoregression (VAR) with adaptive model order estimation, as well as a modern Deep Neural Network (DNN) approach for the massive-MIMO channel estimation de-noising problem. The developed methods and signal pre and postprocessing steps are described, followed by their performance evaluation in a set of realistic simulations. The designed algorithms provide results outperforming the baseline spatio-temporal windowing approaches by approximately equal to 2dB effective Downlink (DL) Signal-to-Interference-plus-Noise Ratio (SINR) metric in single and multi-user MIMO scenarios. Extensive simulation results demonstrate the robustness of the developed methods to the dynamic channel conditions.

Coherent multi-band MIMO radar: robustness analysis to SSMF-based RF signal delivery.

A numerical evaluation is conducted to assess the impact of distributing radio frequency (RF) signals through optical fiber links on the performance of a coherent multi-band multiple-input multiple-output (MIMO) radar system. The analysis focuses on scenarios where the antennas are widely separated in comparison to the employed signal wavelengths. The development of a model to quantify the phase noise (PN) induced on each RF band due to the signal transmission through optical fiber links between the centralized base station and each radar peripheral is described. Monte Carlo simulation results are collected to estimate the key performance indicators (KPIs) for varying standard single-mode fiber (SSMF) length and different PN contributions. The main contributors to the PN are revealed to be chromatic dispersion (CD), double Rayleigh scattering (DRS), and mechanical vibrations. In a shipborne scenario, a significant performance degradation occurs only when the length of the fiber links reaches approximately 20 km. Further, the PN impact has also been studied in a shipborne scenario to analyze the robustness of the system for worse phase noise level assumptions. The results reveal excellent robustness of the proposed centralized acquisition and processing approach in the presence of both very long fiber links and economically employed RF oscillators.

Channel estimation for reconfigurable intelligent surface‐assisted mmWave using multidimensional Runge Kutta orthogonal matching pursuit algorithm in MIMO systems

SummaryIn the context of Reconfigurable Intelligent Surface (RIS) assisted millimeter wave communications within the Multiple Input Multiple Output (MIMO) system, Channel Estimation (CE) poses a significant challenge due to signal sparsity and blockages between transmitters and receivers. Consequently, communication faces difficulties without a direct line‐of‐sight path, leading to increased pilot overhead. To address these obstacles, this research introduces a channel estimation approach for RIS‐aided millimeter wave communication employing the Multidimensional Runge Kutta Orthogonal Matching Pursuit (MRKOMP) algorithm in MIMO systems. The proposed method aims to enhance network coverage and channel capacity for efficient data transmission. By utilizing the multiscale cumulative residual distribution entropy function, the method establishes an indirect path when the line‐of‐sight is obstructed. Furthermore, the multidimensional orthogonal matching pursuit methodology calculates sparsity levels and facilitates sparse recovery. Optimization of the weight parameter through the Runge Kutta optimizer effectively eliminates sparsity, resulting in improved spectral efficiency and RIS gain. Simulation results demonstrate a significant 99.9% enhancement in network capacity and spectral efficiency compared to existing methods.

Dual-port circularly polarized dielectric resonator–based antenna with reconfigurable integrated multifunctional filter for 5G cognitive radio applications

Abstract In this paper, the design of a circularly polarized (CP) multiple-input–multiple-output (MIMO) antenna system is presented, utilizing a dielectric resonator (DR). This presented antenna system is subsequently integrated with a multifunctional filter, all meticulously structured on a single substrate. The multifunctional filter operates in three modes: reconfigurable band-pass and band-reject filter as well as all-pass filter. The overall structure works as a tunable filtenna. The designed filtenna is expanded into a two-port MIMO system on a unified substrate, providing strong port isolation below −28.5 dB. The overall dimension of proposed radiator is 180 × 180 × 1.6 mm3. The value of peak gain is 5.19 dBic. By switching the states of PIN diodes, the designed filtenna operates as a sensing and communicating antenna for interweave and underlay cognitive radios (CRs). The proposed antenna supports the CP waves within the working band, i.e., 3.6–4.5 GHz. The simulated results are validated by comparing them with the measured results showing less variation among them. MIMO parameters, including the envelope correlation coefficient and diversity gain, have been calculated for the proposed filtenna, representing its suitability for 5G-CR applications.

Combining 77–81 GHz MIMO FMCW radar with frequency-steered antennas: a case study for 3D target localization

Abstract In this paper, we introduce a compact 6 × 8 channel multiple-input multiple-output frequency-modulated continuous-wave radar system capable of determining the three-dimensional positions of targets despite utilizing a linear virtual array. The compact system, containing two cascaded radar transceiver ICs, has 48 virtual channels. We conduct a direction of arrival estimation with these virtual channels to determine the azimuth angle. To overcome the spatial limitation of the linear array, we use frequency-steered transmit antennas, which vary their main lobe direction during the frequency chirp, allowing the elevation angle to be determined by using a sliding window fast Fourier transform algorithm. In this study, we present the system’s concept along with the associated signal processing. By taking measurements in different scenarios, each with differently placed corner reflectors, we investigate the capability of the system to separate adjacent targets concerning range, azimuth, and elevation. These measurements are additionally employed to point out the design trade-offs inherent to the system.

A Survey on Beamspace Millimeter-wave Massive Mimo Systems: An Overview of Open Issues, Challenges, and Future Research Trends

Abstract: The channel sparsity is exploited by transforming the channel into an angular domain from the spatial domain at millimeter wave frequencies in Beamspace Multiple-Input Multiple-Output (B-MIMO) systems. Every beam in B-MIMO corresponds to an individual radio frequency (RF) chain, and thus, beam selection techniques can reduce the RF hardware complexity. RF chain limitation is needed for an ideal beamspace precoding scheme to attain good system performance across the entire bandwidth. New methods of beamspace channel estimation with a small amount of estimation error and low computational complexity are challenging in the beam domain due to the larger antenna arrays deployed. A novel beamspace processing technique to improve the direction of arrival estimation performance for different array configurations with improvement in system performance is desired in a 5G system. A detailed review of the B-MIMO system with its architecture and various beam selection techniques is presented in this paper. The scope of the work is elaborated by incorporating various existing beamspace precoding and channel estimation methods. The paper also presents various beamspace processing schemes for parameter estimation with a focus on the direction of arrival estimation performance.

Optimizing the fuzzy PI controller for quadruple tank system using multi-objective Jaya algorithm

This article proposes the Jaya optimization algorithm for the multi-objective optimization of parameters for a Fuzzy Proportional-Integral (Fuzzy PI) controller, referred to as MOJaya. The purpose is to control the water level of a quadruple-tank system (QTS). The controlled plant is a nonlinear and uncertain Multiple-Input Multiple-Output (MIMO) system, which is affected by various factors such as parameter uncertainties, external disturbances, and significant time delays. These factors have a considerable impact on the stability of the control system. The parameters of the fuzzy controller and the normalization coefficients of the Fuzzy PI controller are optimized offline using the multi-objective Jaya optimization (MOJaya) algorithm. To demonstrate the effectiveness of the proposed algorithm, it was compared with the Fuzzy PI and PI-MOJaya algorithms. The simulation results indicate that the proposed algorithm outperforms the other algorithms in terms of control quality.

Key Enabling-Technologies of 4G and 5G Network

This paper provides a comprehensive overview of Multiple Input Multiple Output (MIMO) technology and its pivotal role in enhancing 4G (LTE) networks. MIMO technology, characterized by the use of multiple antennas at both the transmitter and receiver, enables significant improvements in data rates, coverage, and spectral efficiency. The key functionalities of MIMO technology in 4G LTE networks are elucidated, including spatial multiplexing, improved signal quality through spatial diversity, increased coverage via transmit diversity, and enhanced spectral efficiency. MIMO technology enables increased throughput and supports bandwidth-intensive applications like file downloads and video streaming by broadcasting many data streams simultaneously over the same frequency range. Furthermore, the adaptive nature of MIMO systems is discussed, highlighting their ability to dynamically adjust antenna configurations and employ adaptive beam forming techniques. These features optimize performance in real-time, maximizing signal strength, reducing interference, and enhancing overall network reliability. In summary, this paper underscores the critical importance of MIMO technology in 4G LTE networks, showcasing its ability to deliver high-speed broadband services, improve coverage in challenging environments, and efficiently utilize the available spectrum. As the backbone of modern mobile communications, MIMO-enabled 4G networks provide users with seamless connectivity and support the growing demand for data-intensive applications

Performance Analysis of Power Allocation and User-Pairing Techniques for MIMO-NOMA in VLC Systems

In this paper, we evaluate the performance of multiple-input multiple-output (MIMO) communication systems applied with a non-orthogonal multiple access (NOMA)-based indoor visible light communication (VLC). We present two efficient user-pairing algorithms for NOMA in VLC, aiming to enhance achievable data rates effectively. Our investigation involves the application of three low-complexity power allocation techniques. Comparative analysis reveals performance enhancements when employing the proposed schemes, especially when contrasted with NOMA without user pairing and orthogonal frequency division multiple access (OFDMA). Additionally, we explore the performance of both algorithms in scenarios with both even and odd numbers of users. Simulation results demonstrate the superiority of NOMA in comparison to OFDMA.

Orthogonal beamforming technique for massive MIMO systems

AbstractBeamforming represents a pivotal technology in massive multiple-input multiple-output (MIMO) systems, as it facilitates the regulation of transmission and reception operations. Beamforming techniques’ categorization is based either on their hardware architecture or implementation strategy. This paper proposes an orthogonal beamforming technology founded on a specific implementation method that utilizes predetermined orthogonal beams to serve users. The suggested approach incorporates numerous orthogonal beams relying on a substantial number of antennas at the base station. The primary objective of this approach is to enhance the performance of massive MIMO systems by augmenting spectral efficiency and accommodating more users. The proposed beamforming approach is well suited for millimeter frequency bands. The purpose of this paper is to explore the suggested orthogonal beamforming technology. The concept of this approach is described at first and then followed by an evaluation of its efficacy for a single user through the allocation of orthogonal beams. The suggested approach is also examined in the context of multiuser systems, and the results are compared with the adaptive ZF beamforming technique. Furthermore, the paper presents solutions to the issues that may arise in multiuser systems, for example, ensuring that each orthogonal beam is assigned to only one user. The simulations conducted in this study demonstrate that the suggested approach outperforms the ZF technique in terms of both the spectral efficiency and the number of serviced users. Specifically, the suggested approach can enhance SE by approximately 40.6% over the ZF technique, and it can support up to double the number of users when compared to the ZF approach.

Enhanced Gain Difference Power Allocation for NOMA-Based Visible Light Communications

With the escalating demand for high-data-rate wireless services, visible light communication (VLC) technology has emerged as a promising complement to traditional radio frequency wireless networks. To further enhance the achievable rate and error performance in non-orthogonal multiple access-based VLC downlinks, an efficient power allocation scheme named enhanced gain difference power allocation (EGDPA) is proposed for a multiple-input multiple-output VLC system. The power factors are determined by considering users’ channel gains and utilizing the residual allocation principle, which focuses on the remaining power available after allocating it to the previous users. In addition, the impacts of the user distribution and transmission power are investigated, and the performance metrics in terms of achievable data rate, energy efficiency, and bit error rate are also analytically presented. Simulation results demonstrate that energy efficiency can be significantly improved and the achievable data rate gain can be enhanced by at least 6.25% with the proposed EGDPA scheme as compared with other traditional methods, confirming its superiority and validity for efficient multi-user accessing.

Wideband MIMO configuration with mutual coupling reduction and better isolation using the combination of electric field LC resonator and Swastik resembled structure

This work explains the design and development of a small, wide-ranging antenna for millimeter wave (mm-Wave) and 5G technology and their applications, along with the configuration to set up as a multiple-input multiple-output (MIMO) system. Rogers RT/duroid 5880 substrate material, which has a height of 1.575 mm, is used for fabricating the antenna. It possesses a compact overall size of 36 × 36 mm2. The design process began with the creation of a octagonal conventional mm-wave microstrip antenna. Subsequently, the impedance bandwidth was enhanced by incorporating the slots of rectangular shape on either parallel edges to the patch feed enabling it to perform over a larger bandwidth ranging between 25 GHz to 32 GHz. Moreover, a new metasurface and a structure shaped like a swastika are positioned between the 2 × 2 arrangement to minimize mutual coupling. The suggested MIMO antenna provides a wide frequency range, significant gain, minimal interference between antennas, and a low correlation between signal envelopes while also achieving a high level of diversity gain. Furthermore, the proposed study has been compared to similar works to demonstrate the significance of the proposed research.

Design a hybrid meta-heuristic algorithm for optimal multicell-MMSE to maximize the spectral efficiency in massive MIMO systems

Abstract Due to many capabilities, “massive multiple-input multiple-output (MIMO) systems” are regarded as a crucial enabling innovation. High energy economy, great spectral efficiency (SE), and simultaneous communication to many user equipments (UEs) are some of the sophisticated characteristics of massive MIMO systems. Huge MIMO, which involves installing arrays of antennas with a high amount of active components at the base station (BS) and utilizing coherent baseband processing, is a viable method for boosting the SE of cell phone networks. Massive MIMO’s spatial multiplexing and unparalleled array gain can increase the processing power of cellular networks. Since its origin, it has been assumed that when the number of radios increases infinitely, the coherent interference brought on by pilot emissions leads to a limited capacity limit. To achieve this objective, an optimal multicell MMSE is proposed for SE maximization. It is processed as the precoding or combining technique that is considered the small amount of spatial channel correlation, more capacity and more number of antennas, large-scale fading variations, and pilot contamination. It is noted that several cases for increasing the SE, thus it contain multiple antenna information. The prime novelty of this paper is introducing the hybrid heuristic algorithm, named as Fitness-condition of red deer and rat swarm algorithm (FRDRSA) for providing the best solution. Finally, the work performance that produced the extensive findings is examined. On the other hand, the suggested method produces an impressive result when measuring the system’s overall SE.