Multiple-input Multiple-output Systems Research Articles

Exact Analysis of MIMO Channel Estimation Based on Superimposed Training

In this paper, channel estimation capabilities of a multiple-input multiple-output (MIMO) system using superimposed training sequences are investigated. A new expression for estimation-error variance is derived. It is shown that the training sequences must be balanced and must have specific correlation properties. The latter are required only in a specific zone. Sequences that satisfy these criteria exist and are referred to as zero-correlation zone (ZCZ) solutions. Consequently, by using balanced ZCZ sequences, harmful direct current (DC) offset can be removed. Owing to their zero-cross correlation, interference from other transmitting antennas may be eliminated. Furthermore, a closed-form expression of the estimation-error variance can be obtained due to their impulse-like autocorrelation. To increase the number of antennas in the MIMO system, a new construction of ZCZ sequence set is proposed, in which all sequences are balanced

Semi-blind sparse channel estimation using regularized expectation maximization

In Massive multiple-input multiple-output (MIMO) systems, channel estimation is crucial. The large size of the antennas causes a significant pilot and feedback overhead, making it challenging to estimate channels in massive MIMO systems. Besides, the studies have shown that mmWave channels are sparse due to the limited number of dominant propagation paths. Therefore, the motivation of this paper is to exploit the inherent sparsity of the massive mmWave MIMO channels to develop a semi-blind channel estimation with reduced number of pilots. To this goal, an expectation maximization (EM) based technique has been developed which leverages the sparsity of the underlying channel for its better estimation. An iterative approach is proposed to solve the modeled problem which simultaneously updates the channel coefficients and data symbols using available data and system structure at each iteration. The proposed method imposes sparsity with the aid of Smoothed L0 norm (SL0) in the M-step. The simulation results demonstrate the proposed method have quick convergence and lower channel estimation error compared to the existing methods. As a quantitative evaluation, the proposed method attains the normalized mean square error of 9×10−2 and 7×10−3 at SNR=5dB and SNR=15dB, respectively.

Spectral Efficiency Maximization for Mixed-Structure Cognitive Radio Hybrid Wideband Millimeter-Wave Transceivers in Relay-Assisted Multi-User Multiple-Input Multiple-Output Systems.

This paper proposes a cognitive radio network (CRN)-based hybrid wideband precoding for maximizing spectral efficiency in millimeter-wave relay-assisted multi-user (MU) multiple-input multiple-output (MIMO) systems. The underlying problem is NP-hard and non-convex due to the joint optimization of hybrid processing components and the constant amplitude constraint imposed by the analog beamformer in the radio frequency (RF) domain. Furthermore, the analog beamforming solution common to all sub-carriers adds another layer of design complexity. Two hybrid beamforming architectures, i.e., mixed and fully connected ones, are taken into account to tackle this problem, considering the decode-and-forward (DF) relay node. To reduce the complexity of the original optimization problem, an attempt is made to decompose it into sub-problems. Leveraging this, each sub-problem is addressed by following a decoupled design methodology. The phase-only beamforming solution is derived to maximize the sum of spectral efficiency, while digital baseband processing components are designed to keep interference within a predefined limit. Computer simulations are conducted by changing system parameters under different accuracy levels of channel-state information (CSI), and the obtained results demonstrate the effectiveness of the proposed technique. Additionally, the mixed structure shows better energy efficiency performance compared to its counterparts and outperforms benchmarks.

Joint velocity and angle estimation for single-cycle TDM MIMO automotive radar

Time-division multiplexing (TDM) multiple-input multiple-output (MIMO) radar systems have gained popularity in automotive applications, thanks to their high-resolution and cost-effective properties. However, when the relative motion between the target and the radar is significant and possibly fast time-varying, TDM MIMO systems suffer for reduced maximum unambiguous velocity and angle-Doppler coupling. In this work, a novel off-grid estimation method for TDM MIMO systems is developed that operates on a single cycle and is able to accurately estimate unambiguously the instantaneous velocity and the target direction of arrival (DOA). We model the angle-Doppler coupling through a space-time steering vector. Then, an iterative adaptive approach (IAA)-based method is exploited to reconstruct the 2D velocity-angle spectrum. Additionally, we account for off-grid errors when the targets do not lie on the search grids. A coarse-to-fine strategy is proposed to alleviate the computational burden, where a maximum likelihood (ML) estimator iteratively corrects the off-grid velocity and DOA estimates of each target. Simulated and experimental data are processed to demonstrate the effectiveness of the proposed method.

A novel hybrid algorithm for two-dimensional direction-of-arrival estimation in massive MIMO systems

Massive Multiple-Input Multiple-Output (mMIMO) systems have been significantly exploited to enhance mobile network capacity. Increasing the number of antennas provided higher direction-of-arrival (DOA) estimation accuracy, especially when utilizing a two-dimensional multi-signal classification algorithm (2D-MUSIC). However, this algorithm suffers from the heavy computational complexity imposed by the 2D angle search. This paper proposes a novel hybrid algorithm called the 2D-MM, which combines the strengths of the 2D-MUSIC algorithm with the 2D-Minimum Variance Distortionless Response (2D-MVDR) algorithm to reduce the computational complexity using a fast new DOAs search strategy. To be more precise, the new search strategy involves two stages: the 2D-MVDR stage detects the approximate DOAs within a whole angular field of view using as few antennas as possible and snapshots of received data. Then, the 2D-MUSIC stage accurately determines the number and locations (azimuths and elevations) of those sources within the angular sectors in which they appear. Furthermore, the choice of utilizing a uniform circular array (UCA) configuration for representing mMIMO systems is enhanced by introducing a new geometric structure featuring two concentric rings. The simulation results indicate that confining the spectral peak searching process into octant areas with an angular sector spanning π⁄4 instead of the total angular field of view (0–2π) would avoid searching empty areas and then reduce the computational complexity, where the improvement percentage in the proposed algorithm's running time was approaching 80 % when utilized with mMIMO arrays. In contrast, the proposed algorithm proves effective for mMIMO systems, irrespective of the increased number of antennas utilized.

IMPROVING 5G NETWORK PERFORMANCE WITH MIMO TECHNOLOGY USING BEAMFORMING ALGORITHM

The advent of 5G technology aims to revolutionize wireless communication by providing significantly higher data rates, ultra- reliable low latency, and enhanced connectivity. However, optimizing 5G network performance remains a challenging task, particularly in terms of Bit Error Rate (BER) and spectral efficiency. Multiple-Input Multiple-Output (MIMO) technology, coupled with beamforming algorithms, offers a promising solution to these challenges. This study investigates the application of the Fractional Least Mean Square (FLMS) algorithm for beamforming in MIMO systems to improve BER and spectral efficiency in 5G networks. The primary problem addressed is the need for effective interference mitigation and signal enhancement in densely populated environments, which traditional methods struggle to handle. The proposed method utilizes the FLMS algorithm to dynamically adjust the beamforming weights, thereby optimizing the signal reception quality. The algorithm's fractional nature allows for finer adjustments and better adaptation to varying channel conditions compared to conventional LMS algorithms. Simulation results show the efficacy of the proposed method. Specifically, the implementation of the FLMS algorithm in a 4x4 MIMO system shows a reduction in BER by 30% and an improvement in spectral efficiency by 25% compared to traditional beamforming techniques. These numerical values highlight the potential of FLMS in enhancing 5G network performance, making it a viable approach for future wireless communication systems.

Reconfigurable Massive MIMO: Harnessing the Power of the Electromagnetic Domain for Enhanced Information Transfer

The capacity of commercial massive multiple-input multiple-output (mMIMO) systems is constrained by the limited array aperture at the base station, and cannot meet the everincreasing traffic demands of wireless networks. Given the array aperture, holographic MIMO with infinitesimal antenna spacing can maximize the capacity, but is physically unrealizable. As a promising alternative, reconfigurable mMIMO is proposed to harness the unexploited power of the electromagnetic (EM) domain for enhanced information transfer. Specifically, the reconfigurable pixel antenna technology provides each antenna with an adjustable EM radiation (EMR) pattern, introducing extra degrees of freedom for information transfer in the EM domain. In this article, we present the concept and benefits of availing the EMR domain for mMIMO transmission. Moreover, we propose a viable architecture for reconfigurable mMIMO systems, and the associated system model and downlink precoding are also discussed. In particular, a three-level precoding scheme is proposed, and simulation results verify its considerable spectral and energy efficiency advantages compared to traditional mMIMO systems. Finally, we further discuss the challenges, insights, and prospects of deploying reconfigurable mMIMO, along with the associated hardware, algorithms, and fundamental theory.

High-performance MTM inspired two-port MIMO antenna structure for 5G/IoT applications

Abstract This study thoroughly investigates a two-port multiple-input multiple-output (MIMO) antenna system tailored for 5G operation at 28 GHz. The proposed antenna is patched on a Rogers (RT5880) substrate with a relative permittivity of 2.2 and total size of 20×12×0.508 mm3. The mutual relationship between the radiating patches is refined using an H-shaped metamaterial structure to reduce the isolation to –55 dB. A MIMO configuration with attractive features is employed to reduce the envelope correlation coefficient (ECC) to about 0.00062 and the channel capacity loss (CCL) to about 0.006 bits/sec/Hz, while magnify the gain to about 9.39 dBi and the diversity gain (DG) to about 9.995. Additionally, it boasts a compact size with stable radiation pattern. The simulations of the MIMO antenna are executed using CST microwave studio, subsequently validated with Advanced Design System (ADS) for an equivalent circuit model, then measured using Vector Network Analyzer. Discrepancies between measured and simulated results were analyzed, with observed variations attributed to cable losses and manufacturing tolerances. Despite these challenges, a comprehensive comparison with prior research highlights the notable advantages of the proposed design, positioning it as a compelling solution for 5G applications.

Effects of Two-Channel Noise and Packet Loss on Performance of Information Time Delay Systems.

The performance limitations of multiple-input multiple-output (MIMO) information time delay system (ITDS) with packet loss, codec and white Gaussian noise (WGN) are investigated in this article. By using the spectrum decomposition technique, inner-outer factorization, and partial factorization techniques, the expression of performance limitations is obtained under the two-degree-of-freedom (2DOF) compensator. The theoretical analysis results demonstrate that the system performance is related to the time delay, non-minimum phase (NMP) zeros, unstable zeros and their directions in a given device. In addition, WGN, packet loss and codec also impact the performance. Finally, the theoretical results are verified by simulation examples. Simulation results show that packet loss rate and encoding and decoding have a greater impact on system performance.

Revolutionizing connectivity: Unleashing the power of 5G wireless networks enhanced by artificial intelligence for a smarter future

In the context of 5G wireless networks, this paper addresses a crucial concern—data loss in Analog-to-Digital Converters (ADCs) embedded within Multiple Input Multiple Output (MIMO) channels, a prevalent challenge in modern wireless communication systems. Despite the integration of ADCs in MIMO configurations to mitigate this issue, their implementation introduces significant complexities. In response to this, the paper introduces an innovative and efficient approach aimed at enhancing channel estimation in 5G MIMO systems. The proposed methodology relies on a heuristic-based optimization technique within a channel prediction framework. The framework uses feedback information collected through a Cascade Fusion Serial Network (CFSN) to analyze the receiver-side error ratio and estimate the channel coefficients of MIMO systems at the transmitter. An attention mechanism is incorporated into the model design through the use of a Convolutional Neural Networks - Gated Recurrent Units (CNN-GRU) and a Multi-Scaled Stacked Auto encoder. Parameters of Attention CNN-GRU, Stacked Auto encoder, and CFSN are refined using the Whale Optimization method. The optimization objective is to minimize key metrics, including Root Mean Squared Error (RMSE), Bit Error Rate (BER), and Mean Squared Error (MSE) associated with the estimated channel. Experimental evaluations underscore the efficacy of the proposed MIMO model in the context of 5G networks. The results demonstrate improved convergence rates, heightened prediction performance, and reduced computational costs compared to existing methods. This research contributes valuable insights into addressing data loss challenges specific to 5G MIMO systems, providing a pioneering solution that integrates heuristic optimization, advanced neural network architectures, and nature-inspired algorithms for parameter tuning.

A 4-port flexible MIMO antenna with isolation enhancement for wireless IoT applications

This paper presents a conformal, miniaturized, and geometrically simple monopole antenna designed for Vehicle–to–Everything (V2X) communications. The antenna consists of a flexible substrate, radiating patch, ground, and metallic stubs. Meandered lines are added to the U-shaped radiator to achieve the required bandwidth of the antenna. The antenna has |S11|< −10 dB magnitude from 5.06 to 7.24 GHz, attaining a peak low magnitude of–68 dB. The antenna is configured into a 4-port Multiple–Input–Multiple–Output (MIMO) setup to minimize the mutual coupling between its elements. The proposed flexible MIMO antenna offers bandwidth from 5.37 to 7.34 GHz and a peak moderate gain of 4.63 dBi with omnidirectional stable radiation patterns. To improve the mutual coupling, two hollow concentric circular structures, in combination with a pair of stub networks are integrated between the elements of the MIMO system. The transmission coefficient and surface current analysis confirm the effectiveness of the decoupling structure. The presented MIMO antenna is characterized by high isolation, a low envelope correlation coefficient (ECC), and high diversity gain, suitable for V2X MIMO communication scenarios.

Sub-System Architecture for millimeter-wave massive MIMO systems

In this paper, we study the hybrid beamforming design for millimeter-wave (mmWave) massive multiple-input multiple-output (mMIMO) systems. The designing of hybrid beamforming for orthogonal frequency-division multiplexing (OFDM) systems is tasking since its analog beamforming is shared among all subcarriers. We adopt a two-step technique for designing the analog and digital beamforming separately in order to maximize the average achievable energy and spectral efficiency of frequency-selective mmWave mMIMO-OFDM systems. Firstly, the analog beamforming design is based on the viewpoint of sub-systems (SS) and the goal is to optimize the array gain and radio frequency chains. Secondly, the digital beamforming design is carried out by using the regularized channel diagonalization (RCD) and block diagonalization (BD) solutions. On the other hand, the BD solution is modified for single-user. Thus, we propose the use of SS-RCD for multi-user and SS-BD for single-user hybrid beamforming designs. The solutions provide interference suppression but differ in low-SNR performance when communicating to many mobile users via data streams. Simulation results present that our hybrid beamforming design outperforms several other designs.

High Isolation MIMO Antenna System for 5G N77/N78/N79 Bands.

This paper presents a symmetric dual-band multiple-input multiple-output (MIMO) antenna system tailored for fifth-generation (5G) mobile terminals. Operating within the 5G frequency bands N77/N78 (3.4-3.6 GHz) and N79 (4.8-5.0 GHz), the proposed MIMO system achieves high isolation between adjacent antenna elements through slotting and self-decoupling technologies. Antenna elements are strategically positioned on two frames perpendicular to the smartphone's main board. Each antenna element integrates a rectangular microstrip radiator on the inner frame surface, accompanied by a grounded rectangular ring on the outer frame surface. The feed line, situated atop the main board, connects to an external SMA connector located at the main board's bottom. Measurement results reveal isolations exceeding 20 dB for the lower band and 24 dB for the higher band. The fabricated and tested MIMO antenna system demonstrates excellent agreement between simulation and measurement outcomes.

A novel geometric-based bistatic range grouping algorithm for multi-target localization in distributed MIMO radar systems

In this paper, a novel bistatic range grouping algorithm is proposed in order to localize multiple targets in distributed multiple-input multiple-output (MIMO) radar systems. In a distributed MIMO radar, target localization is performed based on bistatic ranges, the distances between antennas through a target, without directional information, and grouping bistatic ranges with respect to the target is a crucial step to estimate multiple targets efficiently. In contrast to the existing grouping schemes that use tentative target estimation or complicated joint bistatic range grouping and target estimation, the proposed algorithm uses only the geometric property of bistatic ranges, resulting in computationally efficient and robust grouping algorithm without noise enhancement, especially for realistic noisy environments where other existing grouping algorithms often fail. The performance of the proposed scheme is confirmed by evaluating the probability of miss detection and the probability of false alarm through numerical simulations for various environments including indirect paths and blocked paths. The root mean square error performance of the multi-target localization using the proposed bistatic grouping algorithm is also presented for realistic noisy environments.

Data‐driven stability margin for linear multivariable systems

AbstractThe notion of stability margin (SM) plays an important role in control engineering. For multiple‐input multiple‐output (MIMO) systems, the classic SM is no longer applicable. The one‐loop‐at‐a‐time analysis method may lead to unreliable SMs. Although some robust SM analysis methods are popular in MIMO systems, they are model‐based or not easy‐to‐use in engineering sometimes. In this paper, gain margin and time‐delay margin are defined for linear MIMO systems, and a corresponding SM analysis method is proposed by utilizing a loop transformation and the small‐gain theorem. Most importantly, a data‐driven method for measuring the defined SMs is also presented. As a frequency‐domain method, this method can be used to experimentally obtain the SMs of MIMO systems on model‐free occasions. The proposed SM analysis method and measurement method are simple and practical. Two simulation studies and an experimental test are performed to illustrate the effectiveness and practicability of the proposed method.

Developing Laboratory Experiments to teach Undergraduate Engineering Students Multi-User Massive MIMO Technology for 5G Cellular Networks

An effective method of facilitating student learning in a laboratory environment is “practice by doing”. The study of modules related to signal-processing for digital communications requires deep mathematical and theoretical foundations but the practice goal is not really emphasized in the undergraduate curriculum at the University of Mauritius. This causes the students to lose interest in the corresponding modules resulting in high rate of failures. In this work, we propose to develop laboratory experiments with a view to bridge the gap between theoretical and practical aspects in the field of Massive Multiple-Input Multiple-Output (MIMO) systems for 5G cellular networks. Various laboratory scenarios are set up that consider a Maximum Ratio Combining (MRC) receiver in the uplink with an uncorrelated Rayleigh fading channel. Moreover, from a signal processing perspective to enhance the student’s understanding, we analyze the efficiency and error performance of Massive MIMO systems with MPSK and MQAM modulation schemes as well as perfect and imperfect channel estimates. The leading industry software package MATLAB R2022 is used to develop all laboratory experiments and the codes are elaborated in this analysis. Data collected from the experiments are used to generate spectral efficiency and error performance curves which can be used for future research. The findings underscore the significance of accounting for both scenarios and illuminate promising avenues for future research in the realm of massive MIMO education and learning. The assimilation of MATLAB® flowcharts for each MRC receiver, MPSK, and MQAM with perfect and imperfect Channel State Information (CSI) adds further depth to the study, ensuring a comprehensive understanding of the intricacies of massive MIMO systems. Ultimately, this contribution helps in the nurturing of expertise such that future generations of wireless communication pioneers can be inspired.

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.

A low-complexity crest factor reduction design for 5G NR sub-6 GHz digital front-end with restricted peak regrowth

In recent years, orthogonal frequency division multiplexing (OFDM) has gained widespread adoption in communication technologies, exemplified by long-term evolution (LTE), 5th generation new radio (5G NR sub-6 GHz), and others. These technologies are instrumental in meeting the increasing demands for high data rates, throughput, and reliable downlink network access. However, a notable challenge associated with OFDM is the high peak-to-average power ratio (PAPR), imposing limitations on the efficiency of power amplifiers that often operate with significant back-offs. To mitigate this issue, the widely employed crest factor reduction (CFR) technique aims to enhance efficiency. This paper introduces a novel CFR methodology utilizing mixed and multi-iterative signal distortion-based techniques. Our approach is versatile, demonstrating effectiveness for low-bandwidth signals like LTE, as well as large-bandwidth signals reaching up to 100 MHz, as seen in 5G NR FR1. Also, digital up-sampling used to achieve the required high sample rate causes the issue of peak regrowth even after the required PAPR reduction achieved by CFR processing, which defeats the purpose of CFR. Our proposed design offers a solution to this challenge, enabling operation at a low data rate before digital up-sampling while effectively mitigating peak regrowth. Importantly, the implementation significantly reduces FPGA resource utilization, alleviating the complexity associated with multiple-input multiple-output (MIMO) radio systems. Demonstrating compliance with 3rd generation partnership project (3GPP) specifications for 5G NR on a real-time testbed, our approach stands as a practical and efficient solution for contemporary communication systems.

Compact design of MIMO antenna with split ring resonators for UWB applications

In recent times, the Multiple-Input Multiple-Output (MIMO) system has played a vital role in wireless communication. MIMO antenna uses multiple antennas on both the sides of the transmitter and receiver. Mutual coupling occurs between two antenna elements to reduce the performance. The passage of current in the same direction on both neighbouring sides of the antennas increases the rate of mutual coupling. To decrease the mutual coupling, Split Ring Resonators (SRR) are located on the MIMO antenna. In this research paper, a MIMO antenna with SRR for Ultra Wide Band (UWB) applications with different frequency notches is proposed. Also, bandwidth and isolation are considered essential metrics for MIMO antennas. In the proposed 4 × 4 MIMO antenna design, SRR is located at the antenna's two sides, and the defected ground structure is on the bottom portion with a permittivity of 4.4 (FR4 substrate). The defected ground structure is proposed with stubs to enhance the characteristics of bandwidth and create notch bands in the frequency range of 3.1 GHz to 10.6 GHz. Placing SRRs adjacent to the feed line enhances the gain performance. The experimental results show that the ECC of the proposed antenna is less than 0.25, DG is higher than 9.8 dB, and the peak realized gain for 9.38 GHz is 11.3 dB, which is more reliable than other antenna designs. Therefore, experimental outcomes of the proposed antenna design enhance the applicability of band notching for wideband communication.

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.