MIMO Channel Research Articles

Compressive sensing-based channel estimation for high mobile systems with delay Doppler effects

In this paper, channel overhead is reduced by exploiting channel sparsity for multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) system. Where, compressive sensing (CS) based dictionary design algorithms has been adopted as a channel estimation technique in high mobile systems with minimal number of pilots, such as high-speed train (HST) systems. A novel framework design of the dictionary-based CS was proposed considering both delay and Doppler effects in order to correctly recover the channel response. The channel under consideration is a 2 by 2 space-time block code (STBC) MIMO channel. Simulation tests according to the international telecommunication union (ITU) channel model demonstrated the suitability of the proposed dictionary for estimating the channel impulse response (CIR) of a liner time varying (LTV) channel with a mobility approaches 675 Km/h related to a Doppler frequency of 1500 Hz and 2.4 GHz carrier frequency. Two CS recovery algorithms were applied; orthogonal matching pursuit (OMP) and basis pursuit (BP), where by about 7 dB gain in signal to noise ratio (SNR) was achieved with mobility of 675 Km/h using OMP as compared to BP at a bit error rate (BER) of with 128 OFDM subcarriers.

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.

On Robust Millimeter Wave Line-of-Sight MIMO Communications With Few-Bit ADCs

This work focuses on providing robust line-of-sight (LoS) spatial multiplexing at flexible communications distances and directions. Considering oblique LoS uniform linear arrays, we first derive the rank-deficient and orthogonal conditions for the LoS MIMO channel matrices. With this discovery, the topology of high spatial-resolution on one side of the link is shown with a wide full-rank-channel guarantee interval over distance and direction variations. Additionally, to reduce the implementation costs and power consumption, we propose to use low amplitude-resolution quantizers at the side of high spatial-resolution. With numerical evaluations on systems having few-bit analog-to-digital converters (ADCs), the proposed system design is shown to simultaneously achieve a higher spectrum efficiency and higher energy efficiency compared to a conventional single-stream high-amplitude-resolution over a wide signal-to-noise-ratio (SNR) range. Furthermore, we investigate channel equalization under the extreme case of using 1-bit ADCs. After providing a new viewpoint on the generalized approximate message passing (GAMP) algorithm from constrained Bethe free energy minimization, our simulations on bit-error-rates show that the GAMP algorithm can significantly reduce the performance degradation due to coarse quantization and can significantly outperform the Bussgang decomposition based linear minimum-mean-square-error estimator, especially at high SNRs.

Principal Component Tracking for Massive MIMO Channels in High Mobility Scenarios With Diagonal Step Size Matrix

The present article proposes a novel adaptive algorithm with an optimal diagonal step size matrix for multi-dimensional channel principal component tracking in two dimensional massive multiple-input and multiple-output (M-MIMO) systems. First, we prove that the weighted subspace algorithm globally converges to the stationary stochastic process’ major eigenvectors. Then, using the maximum likelihood criterion, we optimize the weight coefficient matrix and derive the convergent condition for the step size range in order to maintain the algorithm stability. To accelerate the convergence, we initially suggest the diagonal step size matrix for multi-dimensional eigenvector tracking. Simultaneously, an optimal diagonal step size matrix is derived, which not only accelerates the convergence speed distinctively but also improves the tracking of multi-dimensional eigenvectors. Moreover, the transient behavior during the adaptation process is investigated in a straight-forward way and the relationship between the convergence time constant and the eigenvalues of the received signals is uncovered. Finally, simulations reveal that the proposed approach outperforms established algorithms such as Oja <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{\prime}\text{s}$ </tex-math></inline-formula> , Delmas and gradient descent algorithms. This approach establishes a sound foundation for tracking channel state information in M-MIMO systems with great mobility.

Machine learning-enabled MIMO-FBMC communication channel parameter estimation in IIoT: A distributed CS approach

Compressed Sensing (CS) is a Machine Learning (ML) method, which can be regarded as a single-layer unsupervised learning method. It mainly emphasizes the sparsity of the model. In this paper, we study an ML-based CS Channel Estimation (CE) method for wireless communications, which plays an important role in Industrial Internet of Things (IIoT) applications. For the sparse correlation between channels in Multiple Input Multiple Output Filter Bank MultiCarrier with Offset Quadrature Amplitude Modulation (MIMO-FBMC/OQAM) systems, a Distributed Compressed Sensing (DCS)-based CE approach is studied. A distributed sparse adaptive weak selection threshold method is proposed for CE. Firstly, the correlation between MIMO channels is utilized to represent a joint sparse model, and CE is transformed into a joint sparse signal reconstruction problem. Then, the number of correlation atoms for inner product operation is optimized by weak selection threshold, and sparse signal reconstruction is realized by sparse adaptation. The experiment results show that the proposed DCS-based method not only estimates the multipath channel components accurately but also achieves higher CE performance than classical Orthogonal Matching Pursuit (OMP) method and other traditional DCS methods in the time-frequency dual selective channels.

Indoor Wireless Multipaths Outlier Detection and Clustering

Wireless communication systems have grown and developed significantly in recent years to fulfill the growing demand for high data rates across a wireless medium. Channel models have been used to develop various sturdy wireless systems for indoor and outdoor applications, and these are simulated in the form of datasets. The presence of outliers in clusters has been a concern in datasets, as it affects the standard deviation and mean of the dataset which reduces the data accuracy. In this study, the outliers in the Cooperation in Science and Technology (COST) 2100 MIMO channel model dataset were shifted to the means of the clusters using the Mean Shift Outlier Detection method. Afterward, the data is clustered using simultaneous clustering and model selection matrix affinity (SCAMSMA). The Mean Shift Outlier Detection method identified 52 and 46 multipaths as outliers and improved the clustering accuracy of the indoor scenarios by 3.5% and 0.93%, respectively. It also increased the precision of the clustering based on the decrease in standard deviation of the Jaccard indices from 0.2435 to 0.1807 and 0.3038 to 0.2075.

Analysis of OSTBC-OFDM Combined with Dual-Polarization and Time-Diversity in Millimeter-Wave MIMO Channels with Rain Distortions.

Various destructive weather and physical phenomena affect many parameters in the radio layer (i.e., affecting the wireless paths Over-The-Air (OTA)) of many outdoor-to-outdoor wireless systems. These destructive effects create polarization torsion and rotation of the signals propagating in space and cause the scattering of wireless spatial paths. The direct meaning is a significant degradation in system performance, especially in the Quality-of-Service (QoS). Under these challenging scenarios, intelligent utilization of advanced Multiple-Input-Multiple-Output (MIMO) techniques such as polarization-diversity and time-diversity at the transmitter, as well as at the receiver, and intelligent use of the Cross-Polarization-Isolation (XPI) mechanism, are essential. We prove that combining these techniques with the tuning of the XPI of the antennas creates optimal conditions in the wireless MIMO channels. This combination does not only improve the system’s performance, but also turns the destructive physical phenomena in the spatial-domain, into an advantage. In this article, we focus on formulating a wireless communication MIMO model in millimeter-Wave (mmWave) channels under rain distortions. We demonstrate the optimal use of combining Orthogonal-Space-Time-Coding (OSTBC) and Maximal-Ratio-Receive-Combiner (MRRC) with cross-polarization diversity techniques, that utilize the tuning of the XPI. An analytical exact optimal solution is proposed, that allows the tuning of the leading parameters to achieve global optimal performance, in terms of channel-capacity and Bit-Error-Rate (BER). In addition, we propose a process of approximation of feedback-closed-loop-MIMO. The feedback is employed between the transmitter and the receiver, in the scenario of changes in the channel-response-matrix in-between successive symbol-times. The feedback was designed to acheive global-maximum channel-capacity, while preserving the channel-path orthogonality in order to minimize the BER.

AI-based indoor localization using mmWave MIMO channel at 60 GHz

In recent years, indoor localization using wireless systems has been an important area of research for its applications towards health, security and the tracking of users. A Global Positioning System (GPS) is considered as the best solution for localization for outdoor scenarios but it fails to provide accurate positioning for indoor scenarios. Wi-Fi fingerprinting methods using received signal strength from multiple access points are popular for solving indoor localization problem. As the wireless systems move towards higher frequencies, higher bandwidth and a large antenna array, sensing has also become feasible along with communication, which is an important research area towards 6G named as Integrated Communication And Sensing (ISAC). ISAC relies on sensing parameter estimations, such as estimation of fine range, Doppler and angular information which contains the signature of the surrounding objects. A localization problem can be solved by analysing the sensing parameters. In this paper, we propose a solution for the localization problem for IEEE 802.11ay WLAN systems based on signal processing and Machine Learning (ML) in indoor scenarios. (...)

On the MIMO Capacity With Joint Sum and Per-Antenna Power Constraints: A New Efficient Numerical Method

We propose a semi-closed-form solution to the problem of computing the capacity and optimal signaling for a Gaussian MIMO channel under a joint sum power constraint (SPC) and per-antenna power constraint (PAPC). Existing efficient solutions to this fundamental problem are only applicable to some special cases: multiple-input single-output (MISO) systems, or full column rank MIMO channels with sufficiently high transmit power, or full-rank optimal signaling. For the general case, we present an efficient numerical method to solve the considered problem which does not make any assumptions on the rank of the channel matrix or the maximum transmit power. To achieve this, the considered problem is transformed into an equivalent minimax problem. We then exploit the special structure of the minimax problem to derive a closed-form solution based on a concave-convex procedure (CCP)-like algorithm. Extensive simulation results show that our proposed algorithm outperforms the existing solutions in terms of complexity and generality.

Over-the-Air Design of GAN Training for mmWave MIMO Channel Estimation

Future wireless systems are trending towards higher carrier frequencies that offer larger communication bandwidth but necessitate the use of large antenna arrays. Signal processing techniques for channel estimation currently deployed in wireless devices do not scale well to this “high-dimensional" regime in terms of performance and pilot overhead. Meanwhile, training deep learning-based approaches for channel estimation requires large labeled datasets mapping pilot measurements to clean channel realizations, which can only be generated offline using simulated channels. In this paper, we develop a novel unsupervised over-the-air (OTA) algorithm that utilizes noisy received pilot measurements to train a deep generative model to output beamspace MIMO channel realizations. Our approach leverages Generative Adversarial Networks (GAN), while using a conditional input to distinguish between Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) channel realizations. We also present a federated implementation of the OTA algorithm that distributes the GAN training over multiple users and greatly reduces the user side computation. We then formulate channel estimation from a limited number of pilot measurements as an inverse problem and reconstruct the channel by optimizing the input vector of the trained generative model. Our proposed approach significantly outperforms Orthogonal Matching Pursuit on both LOS and NLOS channel models, and EM-GM-AMP – an Approximate Message Passing algorithm – on LOS channel models, while achieving comparable performance on NLOS channel models in terms of the normalized channel reconstruction error. More importantly, our proposed framework has the potential to be trained online using real noisy pilot measurements, is not restricted to a specific channel model and can even be utilized for a federated OTA design of a dataset generator from noisy data.

PAPR Reduction of OFDM Signals Using Null Space in MIMO Channel for MIMO Amplify-and-Forward Relay Transmission

PAPR Reduction of OFDM Signals Using Null Space in MIMO Channel for MIMO Amplify-and-Forward Relay Transmission

A Partial Channel Reciprocity-Based Codebook for Wideband FDD Massive MIMO

The acquisition of channel state information (CSI) in Frequency Division Duplex (FDD) massive MIMO has been a formidable challenge. In this paper, we address this problem with a novel CSI feedback framework enabled by the partial reciprocity of uplink and downlink channels in the wideband regime. We first derive the closed-form expression of the rank of the wideband massive MIMO channel covariance matrix for a given angle-delay distribution. A low-rankness property is identified, which generalizes the well-known result of the narrow-band uniform linear array setting. Then we propose a partial channel reciprocity (PCR) codebook, inspired by the low-rankness behavior and the fact that the uplink and downlink channels have similar angle-delay distributions. Compared to the latest codebook in 5G, the proposed PCR codebook scheme achieves higher performance, lower complexity at the user side, and requires less feedback. We derive the feedback overhead necessary to achieve asymptotically error-free CSI feedback. Two low-complexity alternatives are also proposed to further reduce the complexity at the base station side. Simulations with the practical 3GPP channel model show the significant gains over the latest 5G codebook, which prove that our proposed methods are practical solutions for 5G and beyond.

MIMO system capacity based on different numbers of antennas

This paper analyzed and compared the ergodic channel capacities of several typical channel systems (SISO, SIMO, MISO, MIMO) in independent identically distributed flat Rayleigh fading channels. The theoretical and simulation results show that the ergodic capacity of the MIMO channel increases linearly with the number of antennas.

OAM Modes Generated from MIMO Channels for a Double Ring Circular Array Antenna

OAM Modes Generated from MIMO Channels for a Double Ring Circular Array Antenna

C-GRBFnet: A Physics-Inspired Generative Deep Neural Network for Channel Representation and Prediction

In this paper, we aim to efficiently and accurately predict the static channel impulse response (CIR) with only the user’s position information and a set of channel instances obtained within a certain wireless communication environment. Such a problem is by no means trivial since it needs to reconstruct the high-dimensional information (here the CIR everywhere) from the extremely low-dimensional data (here the location coordinates), which often results in overfitting and large prediction error. To this end, we resort to a novel physics-inspired generative approach. Specifically, we first use a forward deep neural network to infer the positions of all possible images of the source reflected by the surrounding scatterers within that environment, and then use the well-known Gaussian Radial Basis Function network (GRBF) to approximate the amplitudes of all possible propagation paths. We further incorporate the most recently developed sinusoidal representation network (SIREN) into the proposed network to implicitly represent the highly dynamic phases of all possible paths, which usually cannot be well predicted by the conventional neural networks with non-periodic activators. The resultant framework of Cosine-Gaussian Radial Basis Function network (C-GRBFnet) is also extended to the MIMO channel case. Key performance measures including prediction accuracy, convergence speed, network scale and robustness to channel estimation error are comprehensively evaluated and compared with existing popular networks, which show that our proposed network is much more efficient in representing, learning and predicting wireless channels in a given communication environment.

Physical Beam Sharing Multiuser Systems: Beamforming Designs and OTA Testings

This paper proposes a novel physical beam sharing system to use few radio-frequency (RF) chains to support more users. The concept of beam sharing is to design a beam pattern to simultaneously benefit all users. In this proposed system, every user can receive a number of data streams equal to the number of RF chains at the base station (BS) and achieve full multiplexing gain. A special design example is that the BS can support multiple users simultaneously even with only one RF chain. As a result, the computational and hardware costs of the BS can be significantly reduced due to the decreased RF chains in deploying a dense communications network. We first derive beamforming designs for this proposed system in a MISO channel with one data stream. Then, the results are further extended to MIMO channels with multiple data streams. Simulation results show that the proposed schemes with the derived solutions outperform conventional systems in several interested parameter settings, and have advantages in terms of fairness and complexity compared with NOMA (non-orthogonal multiple access) systems. Moreover, we also implemented 28 GHz hardware platforms with an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$8 \times 8$ </tex-math></inline-formula> antenna array to verify the feasibility and advantages of the proposed schemes as well as measure the resulting beam patterns. The over-the-air (OTA) testings validate the concept of the proposed physical beam sharing and show that it outperforms conventional physical beamforming schemes.

Double IRSs Aided Massive MIMO Channel Estimation and Spectrum Efficiency Maximization for High-Speed Railway Communications

Intelligent reflecting surface (IRS)-enhanced wireless communication is one of the critical technologies of the sixth generation mobile communication, which has recently emerged as an innovative solution to the realization of intelligent transportation. It can meet the increasing demand for high-speed railway (HSR) communications services. In this paper, we consider a train-ground TDD wireless mobile communication paradigm to deploy two IRSs on the roadside and the train roof to assist the wireless transmission for the HSR communication system. To achieve the more significant performance gain, in the uplink transmission phase, we propose the practical channel estimation framework of the MIMO system aided by double IRSs to acquire reliable channel state information (CSI) of each channel. To facilitate the processing of the three-dimension tensor, we leverage the trilinearity of the parallel factor decomposition to decompose the received signal into a two-dimensional matrix. The Doppler frequency offset compensating scheme and channel tracking scheme based on Kalman-Filter are proposed to further decrease the pilot overheads. Then, in the downlink phase, a block coordinate descent (BCD) algorithm is proposed to handle the data rate maximization problem of double IRSs communication system with the estimated CSI. We reformulate the original problem and decouple the double IRSs phase optimization problem into two single IRS phase optimization non-convex problems under the unit-modulus constraint. The Majorization-Minimization (MM) algorithm is proposed to approximate the optimal solution of the two optimization subproblems. Simulation results corroborate the effectiveness of the proposed channel estimation and phase shifts optimization scheme.

On the Capacity of Gaussian MIMO Channels With Memory

The operational capacity of Gaussian MIMO channels with memory was obtained by Brandenburg and Wyner (1974) under certain mild assumptions on the channel impulse response and its noise covariance matrix, which essentuially require channel memory to be not too strong. This channel was also considered by Tsybakov (2006) and its information capacity was obtained in some cases. It was further conjectured, based on numerical evidence, that these capacities are the same in all cases. This conjecture is proved here. An explicit closed-form expression for the optimal input power spectral density matrix is also given. The obtained result is further extended to the case of joint constraints, including per-antenna and interference power constraints as well as energy harvesting constraints. These results imply the information-theoretic optimality of OFDM-type transmission systems for such channels with memory.

A Modified Geometry-Based MIMO Channel Model for Tunnel Scattering Communication Environments

In this study, a modified non-stationary geometry-based scattering model for tunnel vehicle-to-vehicle (V2V) multiple-input multiple-output Ricean fading channels is presented. The proposed channel introduces non-line-of-sight three-dimensional multi-bounced scattering propagation paths under an assumption of equivalent scattering points to depict the tunnel environments, in which the scatterers are randomly distributed on the semi-cylindrical tunnel wall (i.e., cable racks, jetfans, road signs, lighting facilities). Furthermore, the time-varying channel statistics (i.e., the space and frequency correlation functions) resulting from the relative movement between the mobile transmitter and mobile receiver are analyzed. Finally, numerical results show that the space correlations of the proposed model significantly decrease when the multi-bounced scattering rays are taken into account; moreover, when the velocity and the antenna angle γR increase, the correlations decrease therewith, which exhibits good agreement with the existing V2V scattering channel models and measured data in a real tunnel environment, demonstrating the rationality of the underlying channel model.

A Theoretical Analysis of Favorable Propagation on Massive MIMO Channels with Generalized Angle Distributions

Massive MIMO obtains the multiuser performance gain based on the favorable propagation (FP) assumption, defined as the mutual orthogonality of different users’ channel vectors. Until now, most of the theoretical analyses of FP are based on uniform angular distributions and only consider the horizontal dimension. However, the real propagation channel contains full dimensions, and the spatial angle varies with the environment. Thus, it remains unknown whether the FP condition holds in real deployment scenarios and how it impacts the massive MIMO system performance. In this paper, we analyze the FP condition theoretically based on a cluster-based three-dimensional (3D) MIMO channel with generalized angle distributions. Firstly, the FP condition’s unified mathematical expectation and variance expressions with full-dimensional angular integral are given. Since the closed-form expressions are hard to derive, we decompose generalized angle distributions, i.e., wrapped Gaussian (WG), Von Mises (VM), and truncated Laplacian (TL) into the functions of Bessel and Cosine basis by introducing Jacobi-Anger expansions and Fourier series. Thus the closed-form expressions of the FP condition are derived. Based on the above, we theoretically analyze the asymptotically FP condition under generalized angle distributions and then compare the impact of angular spreads on the FP performance. Furtherly, the FP condition is also investigated by numerical simulations and practical measurements. It is observed that environments with larger angle spreads and larger antenna spacing are more likely to realize FP. This paper provides valuable insights for the theoretical analysis of the practical application of massive MIMO systems.