Multiple-input Multiple-output Wireless Channels Research Articles

Measurements and Characteristics Analysis of 6G Ultra-Massive MIMO Wireless Channels With Different Antenna Configurations and Scenarios

In this paper, ultra-massive multiple-input multiple-output (MIMO) channel measurements at 5.3 GHz are conducted, including different antenna array configurations and different measurement scenarios. The antenna array configurations include uniform linear array (ULA) and distributed uniform linear array (DULA). The measurement scenarios include single-user fixed-to-fixed (F2F), multi-user F2F, and single-user fixed-to-mobile (F2M) measurement scenarios. The statistical properties of ultra-massive MIMO channels are studied, including delay power spectral density (DPSD), space cross-correlation function (SCCF), angle cross-correlation function (ACCF), frequency correlation function (FCF), root mean square (RMS) delay spread (DS), RMS angle spread (AS), singular value spread (SVS), scalar product (SP), level crossing rate (LCR), average fading duration (AFD), Doppler power spectral density (PSD), etc. The specific ultra-massive MIMO channel characteristics are investigated and validated by channel measurements, including spatial non-stationarity, spherical wavefront behavior, channel hardening, and sparse properties in the angle domain. In addition, channel capacities are investigated. The channel measurement results will be a great importance for ultra-massive MIMO communication system deployments.

Three-Dimensional Image Transmission of Integral Imaging through Wireless MIMO Channel.

For the reconstruction of high-resolution 3D digital content in integral imaging, an efficient wireless 3D image transmission system is required to convey a large number of elemental images without a communication bottleneck. To support a high transmission rate, we herein propose a novel wireless three-dimensional (3D) image transmission and reception strategy based on the multiple-input multiple-output (MIMO) technique. By exploiting the spatial multiplexing capability, multiple elemental images are transmitted simultaneously through the wireless MIMO channel, and recovered with a linear receiver such as matched filter, zero forcing, or minimum mean squared error combiners. Using the recovered elemental images, a 3D image can be reconstructed using volumetric computational reconstruction (VCR) with non-uniform shifting pixels. Although the received elemental images are corrupted by the wireless channel and inter-stream interference, the averaging effect of the VCR can improve the visual quality of the reconstructed 3D images. The numerical results validate that the proposed system can achieve excellent 3D reconstruction performance in terms of the visual quality and peak sidelobe ratio though a large number of elemental images are transmitted simultaneously over the wireless MIMO channel.

MIMO Channel Estimation Using Score-Based Generative Models

Channel estimation is a critical task in multiple-input multiple-output (MIMO) digital communications that substantially effects end-to-end system performance. In this work, we introduce a novel approach for channel estimation using deep score-based generative models. A model is trained to estimate the gradient of the logarithm of a distribution and is used to iteratively refine estimates given measurements of a signal. We introduce a framework for training score-based generative models for wireless MIMO channels and performing channel estimation based on posterior sampling at test time. We derive theoretical robustness guarantees for channel estimation with posterior sampling in single-input single-output scenarios, and experimentally verify performance in the MIMO setting. Our results in simulated channels show competitive in-distribution performance, and robust out-of-distribution performance, with gains of up to 5 dB in end-to-end coded communication performance compared to supervised deep learning methods. Simulations on the number of pilots show that high fidelity channel estimation with 25% pilot density is possible for MIMO channel sizes of up to 64 × 256. Complexity analysis reveals that model size can efficiently trade performance for estimation latency, and that the proposed approach is competitive with compressed sensing in terms of floating-point operation (FLOP) count.

Research on Vehicle-to-Vehicle MIMO Wireless Channels in Various Tunnels

In this paper, we propose a geometry-based channel model for multiple-input multiple-output (MIMO) vehicle-to-vehicle (V2V) communications in practical tunnel scenarios. In the proposed channel model, the waves transmitted from the transmitter experience line-of-sight (LoS) and non-LoS (NLoS) before arriving at the receiver. The time-varying parameters of the propagation paths and angles are derived to characterize the channel non-stationarity. Furthermore, we derive the correlation property functions of the proposed V2V channel model; they are space cross-correlation functions (CCFs), temporal auto-correlation functions (ACFs), frequency CCFs, as well as the Doppler power spectrum distributions (PSDs). Numerous simulation results of the propagation characteristic for different physical properties of the tunnel, as well as the different motion properties of the transmitter and receiver, are studied and discussed; they demonstrate that the proposed channel model is suitable for describing the practical V2V tunnel communication scenarios.

Evaluation of an Antenna Selection Strategy for Reduced Massive MIMO Complexity

AbstractMassive Multiple‐Input Multiple‐Output (MIMO) is emerging as one promising technology for the fifth generation (5G), but the hardware and software complexity arising from the sheer number of transmitting elements is a bottleneck. Antenna selection strategies have been reported as an appealing solution for hybrid beamforming architectures to select a number of radio‐frequency (RF) chains less than the total number of antennas but are yet to be fully defined and evaluated. In this work, a generic strategy relying on the receiver (Rx) spatial correlation is investigated to select the best antenna subset from a full array. It is evaluated from ray‐traced massive MIMO radio channels using propagation metrics, and also sum‐rate capacity computation. The results demonstrate that (1) for a fixed number of users, a subset with wisely selected distributed elements outperforms a collocated one for all studied metrics with performance close to the full array and (2) using a proposed optimization algorithm for a dynamic number of users, the number of active Tx antennas can be further optimized for the different studied subsets. Moreover, for a fixed number of users, the best antenna subset presenting the lowest Rx correlation values is found to reach the optimal sum‐rate capacity using simple linear precoders compared with dirty paper coding. This is achieved with only a third of the initial RF chain hardware complexity, thus validating the proposed approach. Also, experimental measurements, presented for an industrial scenario, validate the proposed approach.

Measurement-Based Feasibility Exploration on Detecting and Localizing Multiple Humans Using MIMO Radio Channel Properties

This paper explores the feasibility of using the multiple-input multiple-output (MIMO) radio channel properties to passively detect and localize multiple humans in indoor environments. We propose to utilize the unique reverberation characteristics of indoor channels for the purpose of detecting, and the power angular delay profile (PADP) for localizing humans. On the one hand, the reverberation time corresponds with the decay rate of multipath in a closed or partially closed cavity, and varies with the change of the number of humans or the moving of humans relative to the antennas at link ends. On the other hand, the PADP is proposed to be calculated by the Multiple Signal Classification (MUSIC) super resolution algorithm with frequency smoothing preprocessing. The proposed approach is evaluated based on real-world MIMO radio channel measurements obtained from a meeting room. Measurements with and without the presence of humans have been conducted, where the maximum number of humans considered is four. Humans facing different directions, either in parallel or orthogonal to the direct line between the transmit and the receive antennas have been taken into account. In term of the detection feasibility, it is found that the change of the number of humans as well as the change of their facing/moving directions inside the partial reverberant region can be reflected on the change of the reverberation time estimated from the power delay profile of channel. In term of the localization feasibility, it is found that single human location can be well associated to the peak of the variation of the PADP during his/her movement, while multiple humans' movements result in obvious power variation in the very vicinity of some of them, and also in the vicinity of some background objects that is far from target humans.

Blind Joint MIMO Channel Estimation and Decoding

We propose a method for multiple-input multiple-output (MIMO) decoding when channel-state information (CSI) is unknown to both the transmitter and receiver. The proposed method requires some structure in the transmitted signal for the decoding to be effective, in particular that the underlying sources are drawn from a hypercubic space. Our proposed technique fits a minimum volume parallelepiped to the received samples. This problem can be expressed as a non-convex optimization problem that can be solved with high probability by gradient descent. Our blind decoding algorithm can be used when communicating over unknown MIMO wireless channels using either binary phase-shift keying or MPAM modulation. We apply our technique to jointly estimate MIMO-channel gain matrices and decode the underlying transmissions with only knowledge of the transmitted constellation and without the use of pilot symbols. Our results provide theoretical guarantees that the proposed algorithm is correct when applied to MIMO systems with four or fewer transmit antennas. Empirical results show small sample size requirements, making this algorithm suitable for block-fading channels with coherence times typically seen in practice. Our approach has a loss of less than 3 dB compared to zero forcing with perfect CSI, imposing a similar performance penalty as space-time coding techniques without the loss of rate incurred by those techniques.

Robust SINR-Constrained Symbol-Level Multiuser Precoding With Imperfect Channel Knowledge

In this paper, we address robust design of symbol-level precoding for the downlink of multiuser multiple-input multiple-output wireless channels, in the presence of imperfect channel state information (CSI) at the transmitter. In particular, we consider two common uncertainty models for the CSI imperfection, namely, spherical (bounded) and stochastic (Gaussian). Our design objective is to minimize the total (per-symbol) transmission power subject to constructive interference (CI) constraints as well as users' quality-of-service requirements in terms of signal-to-interference-plus-noise ratio. Assuming bounded channel uncertainties, we obtain a convex CI constraint based on the worst-case robust analysis, whereas in the case of Gaussian uncertainties, we define probabilistic CI constraints in order to achieve robustness to statistically-known CSI errors. Since the probabilistic constraints of actual interest are difficult to handle, we resort to their convex approximations, yielding tractable (deterministic) robust constraints. Three convex approximations are developed based on different robust conservatism approaches, among which one is introduced as a benchmark for comparison. We show that each of our proposed approximations is tighter than the other under specific robustness conditions, while both always outperform the benchmark. Using the developed CI constraints, we formulate the robust precoding optimization as a convex conic quadratic program. Extensive simulation results are provided to validate our analytic discussions and to make comparisons with existing robust precoding schemes. We also show that the robust design increases the computational complexity by an order of the number of users in the large system limit, compared to its non-robust counterpart.

Learning Spatiotemporal Features of CSI for Indoor Localization With Dual-Stream 3D Convolutional Neural Networks

Recently, the research of WiFi-based indoor localization combing with deep-learning techniques has earned wide attention due to its potential applications in smart cities. In this paper, a novel fingerprinting system is proposed to achieve indoor localization via learning spatiotemporal features from channel state information (CSI) of multiple-input multiple-output wireless channels (CSI-MIMO) by a dual-stream three-dimensional (3D) convolutional neural network (DS-3DCNN). In the proposed system, the gathered raw CSI-MIMO data are firstly preprocessed through amplitude outliers elimination and phase sanitization for constructing a pair of 3D CSI-MIMO matrices including a 3D amplitude matrix and a 3D phase matrix. Next, the 3D matrices will be input to the DS-3DCNN deep neural network which consists of two parallel subnetworks with specific architecture of several convolution, batch normalization, max-pooling, and fully connected layers. Through this DS-3DCNN network, learning spatiotemporal features of CSI-MIMO is carried out simultaneously from 3D amplitude and phase matrices. And then, probabilistic classification results of two subnetworks are fused in the final output layer of the proposed DS-3DCNN based on Bayes' theorem. Moreover, in the offline training stage, a dual-stream joint optimization method is presented for efficiently optimizing network parameters. After offline training of the DS-3DCNN, in the online locating stage, current CSI-MIMO data are firstly collected from the mobile device to be located. Then probabilistic classification results are obtained from the output layer of the DS-3DCNN, and further used to approximate the posterior distribution of the mobile device's location with a Gaussian mixture model. Finally, a novel location estimation algorithm is deduced based on the minimum mean square error (MMSE) criterion. Since unique features of wireless MIMO channels are jointly learnt in spatial, temporal, and frequency domains, the proposed DS-3DCNN based fingerprinting system is reasonable to provide accurate localization results in indoor environments, which is verified in corresponding experiments.

Spatial multiplexing gain in MIMO radars with widely separated antennas

Multiple-input multiple-output (MIMO) radars have attracted much attention for their superior ability to enhance a system's performance. In this study, the authors' goal was the study of the spatial multiplexing gain of MIMO radars with widely separated antennas (WS-MIMO), which the authors showed that is equal to the number of unambiguously detectable targets. They obtained this number from two different aspects: first, by defining the ambiguity function of a WS-MIMO radar in the case of multiple targets, suitable for such purpose; Second, by modelling the MIMO radar system with a MIMO wireless channel. They showed that a MIMO radar is indeed a MIMO wireless system communicating the information about the existence of the targets. By such modelling, they could easily make the relation between dual concepts of MIMO radar and MIMO communication, one of which is the multiplexing gain.

Secure minimum loss route selection of MIMO-based MANET in combined (indoor, outdoor, and forest) terrain

Multiple-input multiple-output (MIMO) system is a very promising technique in modern wireless communication systems, which can meet the demand of high data rate with limited bandwidth. Integration of MIMO technology with mobile ad hoc network can improve the performance of transmission process in hazardous environment. In order to design a real MIMO wireless system and predict its performance under certain circumstances, it is necessary to have accurate MIMO wireless channel models for different scenarios. Mobile ad hoc network with multiple antennas suffers scattering effects. A combination of two ring model and random scattering model is described in this paper to evaluate channel impulse response of the network. Channel impulse response or channel matrix is used to estimate the propagation loss of the MIMO-based mobile ad hoc network in different terrain. Finally, a minimum loss secure path selection is described here using path loss-based administrator selection secure routing protocol (PASR) protocol to select loss less secure route for transmission.

Structured-Compressed-Sensing-Based Impulsive Noise Cancelation for MIMO Systems

In this paper, the powerful signal processing theory of structured compressed sensing (SCS) is exploited to overcome the challenge of impulsive noise (IN) cancelation in multiple-input multiple-output (MIMO) systems. To the best of the authors’ knowledge, the SCS theory is adopted for the first time for IN elimination, bridging IN mitigation and MIMO systems for its potential applications in vehicular-related communications. In achieving the SCS-based IN cancelation, the measurements matrix of the IN is first obtained from the null subcarriers in the MIMO system specified by the IEEE 802.11 standards series. The SCS optimization framework is then formulated through the proposed spatially multiple measuring method, by fully exploiting the spatial correlation of the IN signals at different receive antennas. To efficiently reconstruct the IN signal, an enhanced SCS-based greedy algorithm, structured a priori aided sparsity adaptive matching pursuit, is proposed, which significantly improves the accuracy and robustness compared with the state-of-the-art methods. Theoretical analysis is presented to guarantee the convergence and the performance error bound of the proposed greedy algorithm. Computer simulations validate that the proposed scheme outperforms the conventional ones over the wireless MIMO channel.

Two-Dimensional Structured-Compressed-Sensing-Based NBI Cancelation Exploiting Spatial and Temporal Correlations in MIMO Systems

Narrowband interference (NBI) caused by narrowband licensed or unlicensed services is a major concern that constrains the performance of multiple-input multiple-output (MIMO) systems. In this paper, the new and powerful signal processing theory of structured compressed sensing (SCS) is introduced to solve this problem. Exploiting the 2-D spatial and temporal correlations of NBI in MIMO systems, a novel NBI recovery method, i.e., the spatial multiple differential measuring method, is proposed in the framework of 2-D SCS. At each receive antenna, a differential measurement vector is acquired from the repeated training sequences in the IEEE 802.11 series preamble. Then, multiple measurement vectors from all receive antennas are utilized to recover and cancel NBI using the proposed SCS greedy algorithm of structured sparsity adaptive matching pursuit. Simulation results indicate that the proposed scheme outperforms the conventional schemes over the wireless MIMO channel.

Measurements and modelling for D2D indoor wideband MIMO radio channels at 5 GHz

Based on the indoor wideband multiple-input multiple-output (MIMO) device-to-device (D2D) measurements at 5 GHz with 100 MHz bandwidth carried out in University of Oulu, Finland, this study investigates the characteristics of indoor D2D radio channels with the same antenna height when both of the transceivers are moving or one end is fixed. The received power for the D2D channel is investigated by using double- and multiple-Rayleigh distributions. It turned out that the received powers follow joint Rayleigh and double-Rayleigh distribution as well as approximately Rayleigh distribution in non-line-of-sight and line-of-sight scenarios, respectively. The angular distributions and their correlation of the angle-of-arrival and angle-of-departure in the azimuth and elevation planes are investigated in order to build three-dimension model of Doppler power spectra and compare with the measurements. The channel parameters are derived from the measurements and compared with WINNER A1 scenario to find the differences between the D2D and conventional fixed-to-mobile (CF2M) channels. From the results, one can find the differences between the D2D and CF2M radio channels and better understand the physics for the D2D channel. Meanwhile, the channel parameters derived herein are important in the link and system level simulations for indoor D2D radio systems.

A Semianalytical Performance Prediction Method for an Iterative MIMO MMSE Receiver With Forward and Backward Error Control

This paper proposes a semianalytical performance prediction method for an iterative receiver that processes signals arriving through a frequency-selective multiple-input multiple-output radio channel. Earlier results provide a distribution of the signal-to-interference-plus-noise ratio at the output of a frequency domain soft interference cancellation minimum mean square error equalizer, which is used to approximate the distribution of the log-likelihood ratios (LLRs) of the demapped bits. Mutual information transfer charts establish a bijective relationship between the variance of the LLRs and the mutual information. We assume a 3rd Generation Partnership Project compliant turbo code, whose transfer charts are simulated in advance. By approximating the LLR variance distribution of the demapped equalizer output, we can evaluate the probability of an intersection between an equalizer chart associated with a random channel realization and a fixed decoder chart. This probability provides the frame error rate. Our proposed prediction method allows horizontally and vertically encoded spatial multiplexing schemes. The vertically encoded scheme is complemented by the inclusion of the Chase combining hybrid automatic repeat request protocol. The proposed method allows reasonable receive antenna correlation.

Performance of opportunistic fixed-beam spatial multiplexing in OFDMA uplink

Adaptive spatial multiplexing (SM) Multiple Input Multiple Output (MIMO) techniques enhance the spectral efficiency of wideband wireless communications in favorable radio channel conditions. In this study, we show the benefits of combining a traditional fixed-beam scheme and multiuser opportunistic radio resource allocation in space-time-frequency domains. This combining is feasible in wideband Orthogonal Frequency Domain Multiple Access (OFDMA) systems such as the Universal Mobile Telecommunications System (UMTS) Long Term Evolution (LTE). This study brings novel knowledge which indicates that an orthogonal fixed beam approach benefits from opportunistic radio resource allocation clearly more than a conventional antenna domain approach in frequency-selective radio channels. It was found out that the fixed beam radio link performance is enhanced in wideband adaptive radio links due to the fact that orthogonal beams reduce correlation between the MIMO channels in the allocated sub-bands. Moreover, beamforming gains bring higher Eigenvalues of the MIMO channel matrix in the opportunistically selected sub-bands. It is shown that the fixed beam deployment changes the distribution of the MIMO channel correlation values and Eigenvalues in a manner which enhances opportunistic multiuser gains in wideband time-frequency-selective MIMO radio channels. Simulation results with 2 × 2 spatial multiplexing in an OFDMA uplink indicate that the proposed beam domain scheme gives up to 80% data throughput gain over the corresponding antenna domain scheme in a pedestrian microcell environment.

Super-Resolution Sparse MIMO-OFDM Channel Estimation Based on Spatial and Temporal Correlations

This letter proposes a parametric sparse multiple input multiple output (MIMO)-OFDM channel estimation scheme based on the finite rate of innovation (FRI) theory, whereby super-resolution estimates of path delays with arbitrary values can be achieved. Meanwhile, both the spatial and temporal correlations of wireless MIMO channels are exploited to improve the accuracy of the channel estimation. For outdoor communication scenarios, where wireless channels are sparse in nature, path delays of different transmit-receive antenna pairs share a common sparse pattern due to the spatial correlation of MIMO channels. Meanwhile, the channel sparse pattern is nearly unchanged during several adjacent OFDM symbols due to the temporal correlation of MIMO channels. By simultaneously exploiting those MIMO channel characteristics, the proposed scheme performs better than existing state-of-the-art schemes. Furthermore, by joint processing of signals associated with different antennas, the pilot overhead can be reduced under the framework of the FRI theory.

MIMO Signal Processing for Mode-Division Multiplexing: An overview of channel models and signal processing architectures

We present the fundamentals of multiple-input, multiple-output (MIMO) signal processing for mode-division multiplexing (MDM) in multimode fiber (MMF). As an introduction, we review current long-haul optical transmission systems and how continued traffic growth motivates study of new methods to increase transmission capacity per fiber. We describe the key characteristics of MIMO channels in MMF, contrasting these with wireless MIMO channels. We review MMF channel models, the statistics derived from them, and their implications for MDM system performance and complexity. We show that optimizing performance and complexity requires management of channel parameters-particularly group delay (GD) spread and mode-dependent loss and gain-by design of transmission fibers and optical amplifiers, and by control of mode coupling along the link. We describe a family of fibers optimized for low GD spread, which decreases with an increasing number of modes. We compare the performance and complexity of candidate MIMO signal processing architectures in a representative long-haul system design, and show that programmable frequency-domain equalization (FDE) of chromatic dispersion (CD) and adaptive FDE of modal dispersion (MD) is an attractive combination. We review two major algorithms for adaptive FDE of MD-least mean squares (LMS) and recursive least squares (RLS)-and analyze their complexity, throughput efficiency, and convergence time. We demonstrate that, with careful physical link design and judicious choice of signal processing architectures, it is possible to overcome MIMO signal processing challenges in MDM systems.

On the Power and Offset Allocation for Rate Adaptation of Spatial Multiplexing in Optical Wireless MIMO Channels

In this paper, we consider resource allocation method in the visible light communication. It is challenging to achieve high data rate due to the limited bandwidth of the optical sources. In order to increase the spectral efficiency, we design a suitable multiple-input multiple-output (MIMO) system utilizing spatial multiplexing based on singular value decomposition and adaptive modulation. More specifically, after explaining why the conventional allocation method in radio frequency MIMO channels cannot be applied directly to the optical intensity channels, we theoretically derive a power allocation method for an arbitrary number of transmit and receive antennas for optical wireless MIMO systems. Based on three key constraints: the nonnegativity of the intensity-modulated signal, the aggregate optical power budget, and the bit error rate requirement, we propose a novel method to allocate the optical power, the offset value, and the modulation size. Based on some selected simulation results, we show that our proposed allocation method gives a better spectral efficiency at the expense of an increased computational complexity in comparison to a simple method that allocates the optical power equally among all the data streams.

Wideband and Ultra-Wideband Characterization of Three-Dimensional MIMO Wireless Channels

In this paper, we compute cross-correlation functions (CCF)s between space-time-frequency (STF) transfer functions of two sub-channels of three-dimensional (3D) outdoor wideband (WB) and ultra-wideband (UWB) multiple-input multiple-output (MIMO) wireless channels. Such a 3D channel modeling is demanding to consider both azimuthal and elevation multipath propagation waveforms. Using the derived CCF and for a multiple-input single-output system (MISO), we calculate coherence bandwidth (CB) and power spectral density (PSD) at mobile station (MS), and in a stationary scenario. Numerical analysis showes that both CB and PSD highly depend the channel bandwidth, while PSD is also influenced by the elevation distribution of multipath waveforms.