Conventional MIMO Research Articles

Enhanced 6G MIMO Signal Detection With Dynamic Sparse Region‐Based Convolutional Neural Network With White Shark Optimization

ABSTRACTMultiple‐input multiple‐output (MIMO) technology is emerging as a promising choice for upcoming sixth‐generation (6G) and beyond‐fifth‐generation (B5G) networks due to the advancements in AI. However, the complexity of MIMO systems, which involve numerous antennas, results in a challenging detection process with high computational demands. To address the interference challenges inherent in large‐scale MIMO technology, this research proposes a 6G massive MIMO signal identification algorithm that integrates a Dynamic Sparse Region‐based Convolutional Neural Network (DSR‐CNN) with White Shark Optimization (WSO) into conventional MIMO receivers. The DSR‐CNN dynamically refines proposal boxes and features, enhancing detection accuracy by effectively handling complex signal patterns. The WSO optimizes the weight parameters within the R‐CNN, improving the algorithm's convergence toward optimal solutions. This hybrid methodology significantly reduces bit error rates (BER) and enhances detection performance, making it ideally suited to the advanced requirements of 6G networks. The proposed approach, DSR‐CNN‐WSO, not only improves accuracy and efficiency but also seamlessly integrates with existing MIMO systems, offering a robust solution for future wireless communication technologies. The proposed method achieves significantly lower BER (0.01) and Mean Square Error (MSE) (0.1), indicating more reliable bit detection and more accurate signal detection.

A novel energy-focused slow-time MIMO radar and signal processing scheme

In this paper, we propose a novel energy-focused slow-time MIMO radar and signal processing scheme, aimed at addressing key challenges in slow-time coding and signal processing technology. Conventional slow-time MIMO radar faces issues such as energy waste due to the omnidirectional transmit beampattern of orthogonal coding and the velocity ambiguity problem. To overcome these limitations, the proposed radar system utilizes a method based on Doppler frequency offset diversity (DFOD) for slow-time coding design. This method enables the adjustment of Doppler offset parameters to achieve a rectangular transmit beampattern with any mainlobe width within a single coherent processing interval (CPI), offering the advantage of low computational complexity. Through an analysis of the ambiguity function for DFOD-based coding, we evaluate both Doppler and angular resolution. To further improve Doppler frequency resolution, a slow-time coding design is introduced based on Pulse Random Permutation (PRP). Subsequently, a signal processing scheme based on matched filtering is presented. To tackle the high Doppler sidelobe issue associated with PRP-based coding, we propose a mismatch filter (MMF) design method utilizing convex optimization. Ultimately, the performance enhancement of the proposed slow-time MIMO radar is verified through simulation analysis in comparison to existing technologies.

CoIR: Compressive Implicit Radar.

Using millimeter wave (mmWave) signals for imaging has an important advantage in that they can penetrate through poor environmental conditions such as fog, dust, and smoke that severely degrade optical-based imaging systems. However, mmWave radars, contrary to cameras and LiDARs, suffer from low angular resolution because of small physical apertures and conventional signal processing techniques. Sparse radar imaging, on the other hand, can increase the aperture size while minimizing the power consumption and read out bandwidth. This paper presents CoIR, an analysis by synthesis method that leverages the implicit neural network bias in convolutional decoders and compressed sensing to perform high accuracy sparse radar imaging. The proposed system is data set-agnostic and does not require any auxiliary sensors for training or testing. We introduce a sparse array design that allows for a 5.5× reduction in the number of antenna elements needed compared to conventional MIMO array designs. We demonstrate our system's improved imaging performance over standard mmWave radars and other competitive untrained methods on both simulated and experimental mmWave radar data.

Provably Energy Efficiency and Lower Power Consumption Based on HOA in 5G MIMO-NOMA Systems

The rapid expansion of 5G communication networks necessitates improved energy efficiency and reduced power consumption. This article explores the integration of Hybrid Optimization Algorithms (HOA) in 5G MIMO-NOMA systems, aiming to enhance energy efficiency and minimize power usage. The proposed methodology leverages MIMO technology and Non-Orthogonal Multiple Access (NOMA). We introduce a new power consumption model based on HOA, recognizing MIMO-NOMA as pivotal in future wireless communication systems. HOA allows simultaneous service for more users, leading to heightened energy efficiency and reduced power consumption compared to conventional MIMO or NOMA systems. A streamlined user admission scheme is presented, admitting users based on ascending power requirements to meet Quality of Service criteria. Numerical results demonstrate the efficacy of HOA and the power allocation strategy in enhancing energy efficiency and user admission. Comparative analysis shows lower power consumption and approximately a 10% increase in energy efficiency compared to traditional methods and other algorithms like GA, PSO, SPPA, and the water-filling algorithm.

Beam-Space MIMO Radar for Joint Communication and Sensing With OTFS Modulation

Motivated by automotive applications, we consider joint radar sensing and data communication for a system operating at millimeter wave (mmWave) frequency bands, where a Base Station (BS) is equipped with a co-located radar receiver and sends data using the Orthogonal Time Frequency Space (OTFS) modulation format. We consider two distinct modes of operation. In <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Discovery</i> mode, a single common data stream is broadcast over a wide angular sector. The radar receiver must detect the presence of not yet acquired targets and perform coarse estimation of their parameters (angle of arrival, range, and velocity). In <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Tracking</i> mode, the BS transmits multiple individual data streams to already acquired users via beamforming, while the radar receiver performs accurate estimation of the aforementioned parameters. Due to hardware complexity and power consumption constraints, we consider a hybrid digital-analog architecture where the number of RF chains and A/D converters is significantly smaller than the number of antenna array elements. In this case, a direct application of the conventional MIMO radar approach is not possible. Consequently, we advocate a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">beam-space approach</i> where the vector observation at the radar receiver is obtained through a RF-domain beamforming matrix operating the dimensionality reduction from antennas to RF chains. Under this setup, we propose a likelihood function-based scheme to perform joint target detection and parameter estimation in Discovery, and high-resolution parameter estimation in Tracking mode, respectively. Our numerical results demonstrate that the proposed approach is able to reliably detect multiple targets while closely approaching the Cramér-Rao Lower Bound (CRLB) of the corresponding parameter estimation problem.

Stacked Intelligent Metasurfaces for Efficient Holographic MIMO Communications in 6G

A revolutionary technology relying on Stacked Intelligent Metasurfaces (SIM) is capable of carrying out advanced signal processing directly in the native electromagnetic (EM) wave regime. An SIM is fabricated by a sophisticated amalgam of multiple stacked metasurface layers, which may outperform its single-layer metasurface counterparts, such as reconfigurable intelligent surfaces (RIS) and metasurface lenses. We harness this new SIM for implementing holographic multiple-input multiple-output (HMIMO) communications without requiring excessive radio-frequency (RF) chains, which is a substantial benefit compared to existing implementations. First of all, we propose an HMIMO communication system based on a pair of SIM at the transmitter (TX) and receiver (RX), respectively. In sharp contrast to the conventional MIMO designs, SIM is capable of automatically accomplishing transmit precoding and receiver combining, as the EM waves propagate through them. As such, each spatial stream can be directly radiated and recovered from the corresponding transmit and receive port. Secondly, we formulate the problem of minimizing the error between the actual end-to-end channel matrix and the target diagonal one, representing a flawless interference-free system of parallel subchannels. This is achieved by jointly optimizing the phase shifts associated with all the metasurface layers of both the TX-SIM and RX-SIM. We then design a gradient descent algorithm to solve the resultant non-convex problem. Furthermore, we theoretically analyze the HMIMO channel capacity bound and provide some fundamental insights. Finally, extensive simulation results are provided for characterizing our SIM-aided HMIMO system, which quantifies its substantial performance benefits, e.g., 150% capacity improvement over both conventional MIMO and its RIS-aided counterparts.

Intelligent Analog Beam Selection and Beamspace Channel Tracking in THz Massive MIMO With Lens Antenna Array

Beamspace multiple-input-multiple-output (MIMO) as a green technology can efficiently substitute for the conventional massive MIMO, provided that the beamspace channel is acquired precisely. The prior efforts in this area of study, especially the learning-driven ones, however, indicate remarkable performance losses owing to a lack of generalization. In this paper, we propose a modified non-linear auto-regressive exogenous (NARX) model for tracking and predicting the beamspace channel over the sequences of time. Benefiting from bounded generalization error, fast convergence, limited prediction variance, and negligible performance loss, the proposed scheme achieves up to 15% spectral efficiency (SE) gain over its counterparts. We further improve this performance by means of an ensemble learning technique for simultaneously training multiple NARX modules in parallel, thus leading to a 23% SE gain. Relying on the predicted beamspace channel, we propose a beamspace analog beam selection technique through fine-tuning the architecture of a pre-trained off-the-shelf GoogleNet, which brings up to 21% SE gain over similar baselines. With the aid of an ensemble learning technique, it is further indicated numerically that up to 34% SE improvement can be achieved, as compared to the counterparts.

Deep learning based MIMO systems using open-loop autoencoder

This article introduces two novel multiple input multiple output spatial division multiplexing (MIMO-SDM) systems based on deep learning techniques using bit-wise (BW) and symbol-wise (SW) open-loop autoencoders (AE), abbreviated as SWAE-MIMO and BWAE-MIMO. Based on the detection methods, we introduce three detectors at the receiver: Radio Transformation Network (RTN), Minimum Mean Square Error (MMSE) technique, and the combination of MMSE technique and neural network (MMSEnet), which can suppress co-channel interference (CCI) among received signal streams resulting in low bit error rates (BER). Furthermore, the considered systems are trained in a single phase to optimally synchronize the transmitter’s and receiver’s learning parameters, thus successfully exploiting spatial diversities to improve the error performance of conventional MIMO systems using the MMSE detector with different numbers of transmit-receive antennas. In a specific case, the BWAE-MIMO system using the RTN detector (BWAE-MIMO-RTN) achieves a BER comparable to that of the conventional MIMO system with a maximum likelihood detector (MIMO-ML) when both systems are equipped with two transmitting and receiving antennas.

Load and Power Indexed Predistortion for Improved Robustness to MIMO Channel Conditions

This letter presents an effective digital predistortion (DPD) solution to the problem of channel-dependent variations in the linearity of multiple-input-multiple-output (MIMO) transmitters. Such variations arise because: 1) conventional MIMO precoding results in large disparities in the average-power levels across chains and 2) antenna crosstalk causes significant active load modulation at the power amplifier (PA) output. Consequently, PA nonlinearity is channel dependent and cannot be compensated using static DPD. In this work, we propose a DPD solution that is a function of the estimated average reflection coefficient at the antenna as well as the PA average input power, which thus accounts for the channel conditions and antenna crosstalk. The proposed solution is validated through experiments on a MIMO transmitter prototype across varying channel conditions.

Leakage Subspace Precoding and Scheduling for Physical Layer Security in Multi-User XL-MIMO Systems

We investigate the achievable secrecy spectral efficiency in a multi-user extra-large multiple-input multiple-output (XL-MIMO) system. In these beyond-5G scenarios, seen as the natural evolution of conventional massive MIMO systems, the distances from the mobile users to the base station become comparable to the antenna array dimensions. We show that the consideration of spherical-wavefront propagation inherent to these set-ups is beneficial for physical-layer security, as it provides immunity against eavesdroppers located in similar angular directions that would otherwise prevent secure communication under classical planar-wavefront propagation. A leakage subspace precoding strategy is also proposed for joint secure precoding and user scheduling, which allows to improve the secrecy spectral efficiency over a 40% compared to conventional zero-forcing methods, under different eavesdropper collusion strategies.

Capacity Analysis and Data Detection of OvTDM-MIMO System

It is well known that the channel capacity of traditional MIMO systems is positively correlated with the number of antennas, so MIMO systems usually use more antennas to meet higher system capacity requirements, which is increasingly conflicting with the miniaturization requirements of portable devices. To overcome the above problems, a new OvTDM-MIMO system is proposed, which can effectively improve the capacity of the system. This system introduces the overlapped time division multiplexing (OvTDM) technology into the MIMO system, which can further improve the channel capacity of the MIMO system by using a smaller number of antennas. This paper introduces the transceiver model of the OvTDM-MIMO system in detail and derives the channel capacity of the system based on mutual information theory. Compared with a conventional MIMO system, the OvTDM-MIMO system has the ability to improve channel capacity. Then, this paper proposes an OvTDM-MIMO symbol detection scheme based on an improved low-complexity Orthogonal Approximate Message Passing (OAMP) algorithm to solve the symbol detection problem caused by symbol correlation. The main design idea of this scheme is to use symbol correlation as the detection constraint and realizes joint symbol detection by combining adjacent symbols, avoiding the problem of excessive matrix size caused by Kronecker product operation. The simulation results show that the proposed detection algorithm can achieve similar performance to traditional MIMO system detection algorithms with low computational complexity.

Decoder Design for Massive-MIMO Systems Using Deep Learning

Designing a massive multiple-input multiple-output (M-MIMO) system is complex, time-consuming, and challenging. Conventional MIMO decoders are either inefficient or complex to implement for M-MIMO scenarios. Deep learning (DL) has recently emerged to perform many complex operations more efficiently within a shorter time. In this article, a DL-based iterative network (DLNet) has been proposed for signal decoding in M-MIMO systems. The proposed DLNet decoder is a 40 layer deep neural network (DNN) that can decode M-MIMO signals for uplink Rayleigh and correlated MIMO channels. With the knowledge of the received signals and the M-MIMO channels, the proposed DLNet decoder decodes the messages of all the users. The DLNet considers complex-valued Rayleigh and correlated MIMO channels while decoding. Its training converges faster than other available deep learning (DL) based MIMO decoders. During simulations, the required number of DNN layers, training iterations, and other network parameters are estimated to improve the proposed decoder's performance. In the M-MIMO perspective, the proposed DLNet has been evaluated for symbol-error-rate (SER) performance, algorithm complexity, and runtime requirement. Simulation results show that the SER of the proposed DLNet decoder performs better by at least 2 dB than other M-MIMO decoding techniques. The DLNet consumes 800 times less time during decoding than other MIMO decoders for equal transmitting and receiving antenna configurations. At the same time, it has 13 times fewer trainable parameters than the available detection-network algorithm.

Plane Spiral OAM Mode-Group Orthogonal Multiplexing Communication Using Partial Arc Sampling Receiving Scheme

A variety of novel orbital angular momentum (OAM)-based communication or sensing systems have attracted much attention over the past decade, the superiority is brought about by nothing other than its orthogonality or vorticity. Plane spiral OAM (PSOAM) mode-group (MG) technique as a reconfigurable beamforming method can be used for building a multiple-in-multiple-out (MIMO) system to achieve the reduction in subchannel correlation, which benefits from the spiral phase distribution within the mainlobe. However, the demultiplexing process still depends on the MIMO algorithm, in which the receiver complexity is same as the conventional MIMO systems. In this article, a PSOAM MG orthogonal multiplexing communication link at the X-band using partial arc sampling receiving (PASR) scheme has been demonstrated experimentally. The MG channels have a good isolation of about 15 dB, and the existing performance can ensure the reliable 16-QAM wireless transmission. Besides, a real-time dual-channel video transmission experiment has been carried out to verify the channel isolation caused by MG’s orthogonality intuitively. More importantly, the demultiplexing procedure using the PASR scheme can be implemented by simple analog phase shifting operation with a lower receiver complexity compared with the conventional MIMO systems.

A Novel Detector based on Compressive Sensing for Uplink Massive MIMO Systems

Massive multiple-input multiple-output is a promising technology in future communication networks where a large number of antennas are used. It provides huge advantages to the future communication systems in data rate, the quality of services, energy efficiency, and spectral efficiency. Linear detection algorithms can achieve a near-optimal performance in large-scale MIMO systems, due to the asymptotic orthogonal channel property. But, the performance of linear MIMO detectors degrades when the number of transmit antennas is close to the number of receive antennas (loaded scenario). Therefore, this paper proposes a series of detectors for large MIMO systems, which is capable of achieving promising performance in loaded scenarios. The main idea is to improve the performance of the detector by finding the hidden sparsity in the residual error of the received signal. At the first step, the conventional MIMO model is converted into the sparse model via the symbol error vector obtained from a linear detector. With the aid of the compressive sensing methods, the incorrectly detected symbols are recovered and performance improvement in the detector output is obtained. Different sparse recovery algorithms have been considered to reconstruct the sparse error signal. This study reveals that error recovery by imposing sparse constraint would decrease the bit error rate of the MIMO detector. Simulation results show that the iteratively reweighted least squares method achieves the best performance among other sparse recovery methods.

Evaluation of Conventional and Imaging MIMO OWC Systems Using Linear Array Design

Multiple-input-multiple-output (MIMO) optical wireless communication systems are potential candidates for high-speed data transmission in indoor environments. In this work, we present an experimental evaluation of two different MIMO systems, i.e., conventional-MIMO (c-MIMO) and imaging-MIMO (i-MIMO) in terms of design considerations. Both systems use the same transmitter configuration featuring a one-dimensional (light-emitting diode) LED transmitting array. On the receiver side, the c-MIMO system employs a similar one-dimensional receiving avalanche photodetector (APD)-based array, whereas the i-MIMO system uses a customized imaging camera-based receiver design. To assess the performance of the two systems, the channel matrix and the bit-error rate are calculated based on the experimental data. At distances shorter than 50 m, the proposed c-MIMO system demonstrates a good transmission performance, particularly when the maximum likelihood detection (MLD) technique is applied. Whereas, at long distances ranging from 40 m to 65 m, the proposed i-MIMO system can ensure an error-free transmission due to the efficient spatial separation between the emitted optical beams. Furthermore, the experimental results confirm that our systems have the potential for high data rates over longer distances in indoor environments.

MIMO Radar Imaging Based on Two-dimensional Compressive Sensing Recovery Algorithm

Conventional sparse signal recovery-based MIMO radar imaging method rearranges the received two-dimensional (2D) signals into a vector, However, transforming the two dimensional equation to one dimensional equation then solving linear equation based sparse signal recovery problem requires huge memory and computational budget. A two-dimensional compressed sensing MIMO radar imaging algorithm based on improved smooth L0 norm is proposed in this paper. Firstly, a two-dimensional MIMO radar imaging model is established to transform the imaging problem into a sparse optimization problem. Then, the two-dimensional negative exponential function sequence is used as the smoothed function sequence to approximate the L0 norm. The two-dimensional imaging of MIMO radar is realized by cyclic iteration and gradient projection algorithm. Simulation results show that the proposed algorithm has advantages over the traditional compressed sensing algorithms.

Range-Ambiguous Clutter Suppression via FDA MIMO Planar Array Radar with Compressed Sensing

Range-ambiguous clutter is an inevitable issue for airborne forward-looking array radars, especially with the high pulse repetition frequency (PRF). In this paper, a method to suppress the range-ambiguous clutter is proposed in an FDA-MIMO radar with a forward-looking planar array. Compressed sensing FDA technology is used to suppress the range-ambiguous clutter and the forward-looking non-uniformity short-range clutter of radar. Specifically, first, the range ambiguous clutter in different regions is separated by the characteristics of the planar array radar elevation dimension and FDA radar range coupling. Meanwhile, regarding the issue of the FDA radar main lobe moving between coherent pulses, a main lobe correction (MLC) algorithm proposes a solution for the issue, where the FDA radar cannot coherently accumulate signals in the case of non-full angle illumination. Finally, compressed sensing technology and elevation dimension filtering are utilized to suppress the range ambiguous clutter at the receiver, with the approach alleviating the range dependence of clutter in the observation region. A small number of clutter snapshots can obtain an approximately ideal clutter covariance matrix through compressed sensing sparse recovery. The method not only reduces the number of training samples, but also overcomes the problem of clutter non-uniformity in the forward-looking array. Therefore, the clutter suppression problems faced by the high repetition frequency airborne radar forward-looking array structure are solved. At the analysis stage, a comparison among the conventional MIMO and FDA methods is carried on by analyzing the improvement factor (IF) curves. Numerical results verify the effectiveness of the proposed method in range-ambiguous clutter suppression.

Weighted Sum Rate Maximization of the mmWave Cell-Free MIMO Downlink Relying on Hybrid Precoding

The cell-free MIMO concept relying on hybrid precoding constitutes an innovative technique capable of dramatically increasing the network capacity of millimeter-wave (mmWave) communication systems. It dispenses with the cell boundary of conventional multi-cell MIMO systems, while drastically reducing the power consumption by limiting the number of radio frequency (RF) chains at the access points (APs). In this paper, we aim for maximizing the weighted sum rate (WSR) of mmWave cell-free MIMO systems by conceiving a low-complexity hybrid precoding algorithm. We formulate the WSR optimization problem subject to the transmit power constraint for each AP and the constant-modulus constraint for the phase shifters of the analog precoders. A block coordinate descent (BCD) algorithm is proposed for iteratively solving the problem. In each iteration, the classic Lagrangian multiplier method and the penalty dual decomposition (PDD) method are combined for obtaining near-optimal hybrid analog/digital precoding matrices. Furthermore, we extend our proposed algorithm for deriving closed-form expressions for the precoders of fully digital cell-free MIMO systems. Moreover, we present the convergency analysis and complexity analysis of our proposed method. Finally, our simulation results demonstrate the superiority of the algorithms proposed for both fully digital and hybrid precoding matrices.

Hybrid Multi Beamforming and Multi-User Detection Technique for MU MIMO System

Multi User—Multiple-input multiple-output based wireless communication system has several advantages over conventional MIMO systems such as high data rate and channel capacity which has drawn great attention recently and is prominently preferred for 5G systems. The other side interferences due to the Multi-User mobile environment such as co-channel interference and multiple access interference the overall system performance will be degraded. Highly reliable techniques need to be incorporated to improve the Quality of services. Moreover, the energy efficiency and compactness requirement of 5G systems present new challenges to investigate techniques for reliable communications. Introducing a novel low-complexity radix factorization based Fast Fourier transform multi beam former and maximal likelihood –multi-user detection techniques. As signal detector tailored with optimal sub-detector systems which results in considerable complexity reduction with intolerable error rate performance. The proposed radix factorized Fast Fourier transform—multi-beam forming architectures have the potential to reduce both hardware complexity and energy consumptions as compared to its state-of-the-art methods while meeting the throughput requirements of emerging 5G devices. Here through simulation results, the efficiency of the scaled ML sub-detector system at the downlink side is compared with the conventional ML detectors. Through experimental results, it is well proved that the proposed detector offers significant hardware and energy efficiency with the least possible error rate performance overhead.

PARAFAC-Direct: A Joint Parameters Estimation Method for Slow-Time MIMO

Aiming at solving the problem that the unambiguous Doppler range of slow-time MIMO is narrower than that of conventional MIMO signals, a new signal processing method based on tensor decomposition is proposed. In the proposed method, a novel tensor model is designed to describe the echo signals of slow-time MIMO radar and retains more useful information which is lost in conventional methods based on Doppler spectrum separation. The proposed algorithm applies PARAFAC to separate target parameters into three matrices, then minimum mean squared error estimation and least squares method are used to estimate the target parameters respectively. Simulation experiments have demonstrated that the proposed method can exploit unambiguous Doppler range, and simultaneously estimate target’s direction of departure, direction of arrival and Doppler frequency without further parameter pairing.