Multiple-input Multiple-output Channel Research Articles

Deep-Unfolded Tikhonov-Regularized Conjugate Gradient Algorithm for MIMO Detection

In addressing the multifaceted problem of multiple-input multiple-output (MIMO) detection in wireless communication systems, this work highlights the pressing need for enhanced detection reliability under variable channel conditions and MIMO antenna configurations. We propose a novel method that sets a new standard for deep unfolding in MIMO detection by integrating the iterative conjugate gradient method with Tikhonov regularization, combining the adaptability of modern deep learning techniques with the robustness of classical regularization. Unlike conventional techniques, our strategy treats the Tikhonov regularization parameter, as well as the step size and search direction coefficients of the conjugate gradient (CG) method, as trainable parameters within the deep learning framework. This enables dynamic adjustments based on varying channel conditions and MIMO antenna configurations. Detection performance is significantly improved by the proposed approach across a range of MIMO configurations and channel conditions, consistently achieving lower bit error rate (BER) and normalized minimum mean square error (NMSE) compared to well-known techniques like DetNet and CG. The proposed method has superior performance over CG and other model-based methods, especially with a small number of iterations. Consequently, the simulation results demonstrate the flexibility of the proposed approach, making it a viable choice for MIMO systems with a range of antenna configurations, modulation orders, and different channel conditions.

Design of a MIMO implantable antenna with ultra-miniaturized volume and reduced SAR

Abstract This paper presents an ultra-miniaturized skin implantable Multiple Input Multiple Output (MIMO) antenna for biotelemetry in the Industrial, Scientific, and Medical (ISM) band (2.4–2.48 GHz). The two-element configuration exhibits a compact planar volume of 4.5 × 6.2 × 0.25 mm³, with an edge-to-edge distance of 0.8 mm and a 75.85 % size reduction. Fabricated on a 0.25 mm thick Roger 10.2LM high permittivity substrate, the antenna achieves a Fractional Bandwidth (FBW) of 9.27 % and a realized gain of −28.2 dBi, with over 24.3 dB isolation across the entire band. Experimental results in multilayer animal tissue aligned well with simulations. Specific Absorption Rate (SAR) analysis at 1 W input power showed values of 848 W/kg (1-g) and 88.46 W/kg (10-g), yielding allowable input powers of 3.77 mW and 45.2 mW, respectively, adhering to international safety guidelines. The 2 × 2 element antenna demonstrated efficient communication over 7 m with a data rate of 78 Mbps/s. Multiple Input Multiple Output channel analysis revealed a Channel Capacity of 10.8 bps/Hz at a 20 dB SNR, nearly double that of an ideal Single Input Single Output system, making it suitable for high data rate skin-implantable applications.

A Deep Learning-based Approach for Channel Estimation in Multi-access Multi-antenna Systems

This paper studies estimating the channel state information at the end of receiver (CSIR) for multiple transmitters communicating with only one receiver so that the latter can decode the incoming signal more efficiently. The transmitters and the receiver are all equipped with multi-antennas and using orthogonal space-time block codes (OSTBC). An algorithm is developed based on deep learning for estimating multi-user multiple-input multiple-output (MU-MIMO) channels. The algorithm could estimate the CSIR using a single pilot block. The proposed convolutional neural network (CNN) architecture designed for this task begins with an input layer that accepts grayscale images, followed by six convolutional blocks for feature extraction and processing. The network concludes with a fully connected layer to output the estimated channel information. It is trained using a regression loss function to map input images to accurate channel information accurately. The performance of the proposed method is compared with classical methods like least square and subspace-based methods, including Capon and rank revealing QR (RRQR) methods. CNN achieved better performance in comparison with the reference. Computer simulations are included to validate the proposed method.

Carrier frequency synchronization for WLAN systems based on MIMO-OFDM-IM

Multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) with index modulation (IM) MIMO-OFDM-IM is a modulation technique that has garnered significant interest in recent times owing to its attraction of smooth transition to greener communications, enhancing efficiency in terms of energy, spectrum and compatibility to previous existing standards without the need for drastic changes in the physical layer. It is also proved that even though OFDM-IM provides better immunity to carrier frequency offset compared to traditional OFDM, the sensitivity to frequency error is still a major issue and it has to be resolved on priority basis to fully exploit the potential offered by the IM technique. In this work, we propose a novel non-data-aided algorithm that efficiently estimates and eliminates the offset present in the received signal. The algorithm has been analyzed for the MIMO-OFDM-IMbased wireless local area network system using the high throughput task group (TGn) MIMO channel models in the presence of AWGN. In addition, it is compared with the other popular blind algorithms based on both pilots and inactive data tones. The simulation plots depict a clear improvement of the new estimator over the existing methods not only in the low signal-to-noise ratio (SNR) regions but also in the high SNR region.

Hard Successive Interference Cancellation for M-QAM MIMO Links in the Presence of Rayleigh Deep-Fading.

In our paper, we propose a generalized version of the Alternating Projections Digital Hard Successive Interference Cancellation (AP-HSIC) algorithm that is capable of decoding any order of constellation M in an M-Quadrature Amplitude Modulation (QAM) system. Our approach applies to Rayleigh deep-fading Multiple-Input Multiple-Output (MIMO) channels with high-level Additive White Gaussian Noise (AWGN). It can handle various destructive phenomena without restricting the number of antenna arrays in the transmitter/receiver. Importantly, it does not rely on closed-loop MIMO feedback or the need for Channel-State Information Transmission (CSIT). We have demonstrated the effectiveness of our approach and provided a Bit Error Rate (BER) analysis for 16-, 32-, and 64-QAM modulation systems. Real-time simulations showcase the differences and advantages of our proposed algorithm compared to the Multi-Group Space-Time Coding (MGSTC) decoding algorithm and the Lagrange Multipliers Hard Successive Interference Cancellation (LM-HSIC) algorithm, which we have also developed here. Additionally, our paper includes a mathematical analysis of the LM-HSIC algorithm. The AP-HSIC algorithm is not only effective and fast in decoding, including interference cancellation computational feedback, but it can also be integrated with any Linear Processing Complex Orthogonal Design (LPCOD) technique, including Complex Orthogonal Design (COD) schemes such as high-order Orthogonal Space-Time Block Code (OSTBC) with high-order QAM symbols.

NOMA‐based precoded quadrature spatial modulation in multiuser MIMO downlink transmission over correlated channel

SummaryIn this paper, a non‐orthogonal multiple access (NOMA)‐based precoded quadrature spatial modulation (PQSM) technique (NOMA‐PQSM) has been proposed for the downlink scenario. In NOMA‐PQSM, two intended receiving antennas are activated at any time instant. One antenna is activated for the in‐phase component of the transmitted signal, and another one is activated for the quadrature phase component, on the basis of data bits. NOMA‐PQSM provides benefits like improved spatial diversity and spectral efficiency in comparison with spatial modulation. This work uses zero forcing (ZF) precoding over downlink flat fading Rayleigh multiple input multiple output (MIMO) channels, to limit the channel's deteriorating effect on transmitted signal, assuming perfect channel state information (CSI) at the transmitter. A low complexity receiver based on the successive interference cancellation is used. An expression for the upper bound of average bit error probability is derived. Moreover, the expressions for the sum mutual information of users and its lower bound are also derived. The proposed scheme is compared with the preprocessing aided spatial modulation (PSM)‐based counterpart. Monte Carlo simulations reveal that the NOMA‐PQSM scheme outperforms its orthogonal counterpart and the PSM scheme.

An adaptive compressive sensing method on hybrid-field channel estimation for a massive MIMO system

Massive MIMO (Multiple-input-multiple output), crucial for 5 G and Beyond 5 G (B5G) networks, faces challenges with terahertz frequencies in B5G as the communication shifts from far-field to near-field. This shift disrupts traditional Massive MIMO channel estimation, leading to increased pilot overhead and limited performance. The current study used the hybrid-field orthogonal matching pursuit (HF-OMP) algorithm from the compressive sensing (CS) framework to estimate the channel with scatters from both far-field and near-field. The method, however, needs more flexibility regarding scatter location in the field of interest. Also, the usage of HF-OMP under a single measurement vector (SMV) scheme limits the overall performance of the existing method. To address these, we have proposed an adaptive hybrid-field simultaneous-OMP algorithm under multiple measurement vector (MMV) of the CS framework which selects multiple atoms having common support within a single iteration. This innovative approach tackles channel estimation for both far-field and near-field scatters while adapting to their varying distributions. Compared to classical methods, simulations show the new algorithm achieves up to a 14 % improvement in normalized mean square error within a 0 dB to 10 dB signal-to-noise ratio range. This translates to significant reductions in pilot overhead and enhanced channel reliability, paving the way for more efficient and robust wireless networks in the future.

High-Speed Rail Multiple-Input–Multiple-Output Channel Model Based on Reconfigurable Intelligent Surface System

This paper presents a geometry-based stochastic model (GBSM) for a reconfigurable intelligent surface (RIS)-assisted multiple-input–multiple-output (MIMO) system tailored to high-speed rail environments, addressing energy leakage at space lattice points caused by the time-dependent reception direction and differing antenna array resolutions. Initially, this study explores the spatial domain feature map and spreading antenna lobe characteristics from various RIS array configurations to reshape the polarization distribution and facilitate a virtual antenna array. Subsequently, the time-dependent reception direction and position are derived by integrating the dynamics of train operations. Finally, the received signal is aligned with the polarization direction to assess the signal reception efficiency and finalize the model output. Research findings indicate that the number of RIS elements, spacing between RIS elements, and mobile relay (MR) movement characteristics significantly influence the performance of the system. Compared to existing models, the proposed model proficiently captures the effects of time-dependent receiver angle properties and RIS array configuration.

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

Optimization of three-dimensional superposed constellation with probabilistic shaping for MIMO visible light communication systems

Superposed constellation technology has received increasing attention due to its robustness to the correlation of multiple-input multiple-output (MIMO) channels in visible-light communication (VLC) systems. In this study, a novel superposed three-dimensional 32-quadrature amplitude modulation (3D-32QAM) constellation scheme combined with probabilistic shaping is proposed for MIMO VLC systems. Tetrahedron-shaped 3D-4QAM and cube-shaped 3D-8QAM signals are transmitted from two separate LEDs, which are superposed at the receiver to obtain a cross-shaped 32QAM signal. In addition to improving the minimum Euclidean distance (MED) by using 3D constellations, the proposed scheme focuses on exploring the advantages of superposed 3D constellations in probabilistic shaping. Both global and local probabilistic shaping can be achieved in terms of superposition of 3D constellations, which increase the MED and reduce the risk of nonlinear distortion of light-emitting diodes, respectively. Experimental results demonstrated that the proposed scheme achieved a better normalized generalized mutual information performance and significantly improved the dynamic range of the driving peak-to-peak voltage when compared with the traditional superposed two-dimensional 32QAM constellation schemes.

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.

Synchronous time multiplexing for non-line-of-sight multiple-input multiple-output optical camera communications.

In typical multiple-input multiple-output (MIMO) optical camera communication (OCC) systems, the spatial correlation of MIMO channels is large. Optical signals between light sources can easily interfere with each other, negatively impacting the overall transmission performance. In this work, we propose a time-multiplexing integral modulation scheme for a non-line-of-sight MIMO OCC system, where each LED transmits different signals to improve both the data rate and security of the system. A genetic algorithm (GA)-based adaptive multi-threshold scheme is designed to demodulate the blurred fringes in multi-level pulse amplitude modulation. The experimental results show that at a distance of 2.5 m, a data rate of 16.4 kb/s can reach with the BER performance of 3.01 × 10-3, which validates the superiority and reliability of our proposed schemes.

Jamming precoding in AF relay-aided PLC systems with multiple eavessdroppers

Enhancing information security has become increasingly significant in the digital age. This paper investigates the concept of physical layer security (PLS) within a relay-aided power line communication (PLC) system operating over a multiple-input multiple-output (MIMO) channel based on MK model. Specifically, we examine the transmission of confidential signals between a source and a distant destination while accounting for the presence of multiple eavesdroppers, both colluding and non-colluding. We propose a two-phase jamming scheme that leverages a full-duplex (FD) amplify-and-forward (AF) relay to address this challenge. Our primary objective is to maximize the secrecy rate, which necessitates the optimization of the jamming precoding and transmitting precoding matrices at both the source and the relay while adhering to transmit power constraints. We present a formulation of this problem and demonstrate that it can be efficiently solved using an effective block coordinate descent (BCD) algorithm. Simulation results are conducted to validate the convergence and performance of the proposed algorithm. These findings confirm the effectiveness of our approach. Furthermore, the numerical analysis reveals that our proposed algorithm surpasses traditional schemes that lack jamming to achieve higher secrecy rates. As a result, the proposed algorithm offers the benefit of guaranteeing secure communications in a realistic channel model, even in scenarios involving colluding eavesdroppers.

On the periodicity of singular vectors and the holomorphic block-circulant SVD on the unit circumference

We investigate the singular value decomposition of a rectangular matrix that is analytic on the complex unit circumference, which occurs, e.g., with the matrix of transfer functions representing a broadband multiple-input multiple-output channel. Our analysis is based on the Puiseux series expansion of the eigenvalue decomposition of analytic para-Hermitian matrices on the complex unit circumference. We study the case in which the rectangular matrix does not admit a full analytic singular value factorization, either due to partly multiplexed systems or to sign ambiguity. We show how to find an SVD factorization in the ring of Puiseux series where each singular value and the associated singular vectors present the same period and multiplexing structure, and we prove that it is always possible to find an analytic pseudo-circulant factorization, meaning that any arbitrary arrangements of multiplexed systems can be converted into a parallel form. In particular, one can show that the sign ambiguity can be overcome by allowing non-real holomorphic singular values.

Intelligent Reflecting Surface Aided MIMO With Cascaded LoS Links: Channel Modeling and Full Multiplexing Region

In this paper, we build up a new intelligent reflecting surface (IRS) aided multiple-input multiple-output (MIMO) channel model, named the cascaded LoS MIMO channel, that is applicable to both near-field and far-field scenarios. The proposed channel model consists of a transmitter (Tx) and a receiver (Rx) both equipped with uniform linear arrays (ULAs), and an IRS is used to enable communications between the transmitter and the receiver through the line-of-sight (LoS) links seen by the IRS. When modeling the reflection of electromagnetic waves at the IRS, we take into account the curvature of the wavefront on different reflecting elements. Based on the established channel model, we show that an IRS-assisted MIMO channel is able to support spatial multiplexing solely by the cascaded LoS links. We generalize the notion of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Rayleigh distance</i> originally coined for the single-hop MIMO channel to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">full multiplexing region (FMR)</i> for the cascaded LoS MIMO channel, where the FMR is the union of all the Tx-IRS and IRS-Rx distance pairs that enable full multiplexing communication. We derive an inner bound of the FMR under a special passive beamforming (PB) strategy named reflective focusing, and provide the corresponding orientation settings of the antenna arrays that achieve full multiplexing.

Modelling the elevation power angle spectrum of a MIMO channel

&lt;p&gt;Multiple-Input Multiple-Output (MIMO) channel modelling is constantly researched, and the innovation of its three-dimensional (3D) models is the introduction of the elevation domain parameters, in particular the elevation angle of arrival (EOA) and elevation angle of departure (EOD) where their power angle spectrum (PAS) and their angular spread (AS) greatly impact the 3D MIMO performance. PAS is the basic characteristic used to estimate and model angular dispersion in wireless channels. Propagating signal waves are constrained into a path having an angle within which the power is contained. One of the scenes that will have a significant effect on these parameters is the relative height between the BS and the building. They will also depend on the distance between the base station (BS) and the user equipment (UE). In this work, we investigate the effect of distance and height on the power angle spectrum of a street canyon and a high-rise scenario in the urban environment. The wireless Insite X3D ray tracing engine and a 3D digital map of the environment were used for the simulation. It is seen that the elevation power spectrum (EPS) in the elevation domain decreases with distance and height in both scenarios for the arrival and departure. The appearance of multiple peaks leads to a double-normal distribution in the arrival of the high-rise as height increases, which is a result of reflections from multiple clusters. The azimuth power spectrum (APS) in the azimuth domain also decreases with distance and height at the arrival but increases with distance and height at the departure. The result of the power angle spectrum in this work can be used for modelling angular dispersion as well as designing adaptive antennas for wireless communication.&lt;/p&gt;

Reconfigurable MIMO antenna: previous advancements and the potential for future wireless communication

Abstract Future smart reconfigurable antennas (RAs) (Haupt R-L and Lanagan M (2013) Reconfigurable antennas. IEEE Antennas and Propagation Magazine 55, 49–61) will likely be fully multipurpose and controlled by software and equipped with machine learning skills that can discern and respond to alterations in the radio frequency environment. Cognitive radio utilizations will be accomplished using a new generation of antenna technology and communication protocols. The effective use of frequencies and the use of polarization diversity and radiation pattern reconfigurability to send data over existing congested frequencies will be major advantages for such applications. The usage of antennas that can be reconfigured in multiple-input multiple-output (MIMO) channels will enhance channel capacity while simultaneously improving channel efficiency and lowering costs (Christodoulou C-G, Tawk Y, Lane S-A and Erwin S-R (2012) Reconfigurable antennas for wireless and space applications. Proceedings of the IEEE 100, 2250–2261). There are a lot of antennas used both at the transmitter and at the receiver front end in a MIMO system. The benefit of employing such arrangements is that different types of information can be conveyed at a similar time, boosting the spectral efficiency of communication in a multipath situation. The coding rate, modulation level, and transmission signaling method of a MIMO system can all be changed in response to changing channel circumstances and user needs. In a MIMO context, polarization reconfigurable/frequency-reconfigurable/radiation pattern RA increase the degree of freedom and enhancing the system’s performance. The usage of such antennas greatly enhances capacity by enabling a choice of various polarization configurations and pattern diversity. Antenna arrays that can be reconfigured are also an appealing MIMO system solution that needs to retain robust communication channels, particularly in portable gadgets where the area is limited.

Antenna Selection for Reconfigurable Intelligent Surfaces: A Transceiver-Agnostic Passive Beamforming Configuration

Reconfigurable intelligent surface (RIS) is capable of improving the wireless system performance by steering the reflected signal in the desired direction. One of the major challenges is that both the transceiver and RIS have to be jointly optimized, where the optimization problems have to be reformulated for different system models and scenarios. To circumvent this challenge, new low-complexity antenna selection (AS) algorithms for transceiver-agnostic RIS configuration are proposed. Given a multiple-input multiple-output (MIMO) channel, the proposed RIS-AS opts for accurately aligning the RIS both with the transmit antenna (TA) and receive antenna (RA) for the sake of maximizing the MIMO channel’s overall output power. The proposed RIS-AS only has to configure the RIS alone, i.e. without iterations with the transceiver optimization. As a result, the proposed RIS-AS has the compelling benefit that they are generically applicable, regardless of the specific transceiver architecture. Our simulation results confirm that the proposed RIS-AS is capable of supporting any MIMO configuration, regardless of their closed/open-loop, single-/full-RF and multiplexing-/diversity-oriented setups.

Parabolic Wavefront Model for Line-of-Sight MIMO Channels

Motivated by the widespread adoption of the parabolic wavefront model for line-of-sight (LOS) multiple-input multiple-output (MIMO) communication, this paper presents a comprehensive analysis of this model’s validity and simple conditions that ensure its applicability. Then, with the model’s scope clearly delineated, the paper expounds a number of properties of the channel that results from applying it. Connections are drawn among these properties under the umbrella of a Fourier interpretation, and their significance to LOS MIMO communication is substantiated.

Pathloss Modeling and Analysis for Intelligent Reflecting Surface-Assisted Terahertz MIMO Wireless Systems

In this paper, the impact of the intelligent reflecting surface (IRS) on the propagation characteristics is analyzed from the arguments of the physical and electromagnetic characteristics of the channel. Analytical expressions for free-space pathloss are derived based on the terahertz multiple-input multiple-output (MIMO) channel model in different communication scenarios. This calculation method is broadly applicable and is used to characterize pathloss as a function of the configuration of our system model. Numerical results show that setting the phase matrix of IRS based only on the angle of arrival (AoA) and angle of departure (AoD) can significantly reduce the pathloss when the far-field condition is met. However, when the distance between the transceiver and the IRS is the same order of magnitude or less than the side length of the IRS, both direction and distance information are required to configure the IRS. This model provides an insightful consideration in the design of IRS-assisted wireless system.