Multiple-input Multiple-output Channel Research Articles

Cooperative Spectrum Sensing under SISO and MIMO Environment for Cognitive Radio Networks

Background: Increasing demand for wireless devices leads to the efficient use of spectrum resources. Despite the limited availability of spectrum resources, all licensed users do not utilize their spectrum all the time. Objective: Cognitive Radio (CR) is used to detect free channels in the available spectrum resources and access them dynamically without interfering with licensed users. Various spectrum sensing (SS) techniques are available to sense the spectrum of licensed users. Methods: The spectrum sensing technique, energy detector (ED), is analysed under a Rayleigh fading channel. Further, Single Input Single Output (SISO) and Multiple Input Multiple Output (MIMO) channel models are developed in the consumer environment. The ED technique is further analysed with cooperative spectrum sensing (CSS) to overcome detection problems for a single CR user. Results: The ED achieves a probability of detection (PD) of 0.9 and a probability of false alarm (PFA) of 0.1 at the lowest SNR of −20 dB, which is the requirement for a standard IEEE 802.22 Wireless Regional Area Network (WRAN). Conclusion: Various receiver operating characteristics are determined by conducting practical simulations and theoretical calculations for each SISO and MIMO channel model with and without the CSS scenario to achieve the target PD and PFA .

Online machine learning-based physical layer authentication for MmWave MIMO systems

Transmitter authentication is significantly critical to secure millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems. By utilizing the mmWave MIMO channel and carrier frequency offset (CFO), this paper proposes a novel online machine learning-based physical layer authentication scheme to validate transmitter identities. In particular, with the help of the Gaussian kernel method and representer theorem, we formulate the physical layer authentication model as a binary classification model. Based on the developed classification model and a constructed convex objective function, we then design an online algorithm to achieve updating authentication parameters, and thus establish a novel online machine learning-based physical layer authentication scheme. Based on the developed authentication model and theories of statistical hypothesis testing, we statistically derive the analytical expressions for false alarm and detection rates for the proposed authentication scheme. Finally, we carry out extensive experiments to verify the authentication performance in terms of the false alarm and detection rates. In addition, we investigate the related authentication efficiency issues (e.g., convergence process) and the capability of resisting against spoofing attacks in mmWave MIMO systems.

Performance analysis of a millimeter wave MIMO channel estimation method in an embedded multi-core processor

The emerging Multi-Processor System-on-Chip (MPSoC) technology, which combines heterogeneous computing with the high performance of field programmable gate arrays (FPGA), is a promising platform for a large number of applications, including wireless communications and vehicular technology. In this specific application context, when multiple-input multiple-output (MIMO) scenarios are considered, the system usually has to manage a large number of communication links among sensors and antennas involving different vehicles and users. Millimeter wave (mmWave) communications are one of the key technology enablers toward achieving high data rates in beyond 5G systems (B5G). Communication at these frequency bands usually involves the use of large antenna arrays, often requiring high computational resources. One of the candidate platforms able to manage a huge number of communications is the Xilinx Zynq UltraScale+ EG Heterogeneous MPSoC, which is composed of a dual-core Cortex-R5, a quad-core ARM Cortex-A53, a graphics processing unit (GPU) and a high-end FPGA. This work analyzes the computational performance that requires a recent mmWave MIMO channel estimation algorithm in a platform of this kind. As a first approach, we will focus our work on the performance that can be achieved via the quad-core ARM Cortex-A53. To this end, we will use the libraries for numerical algebra (BLAS and LAPACK). The results show that our reference implementation is able to manage a large MIMO communication system with 256 antennas without exhausting platform resources.

FUNDAMENTALS OF 5G TECHNOLOGY FOR NEXT GENERATION NETWORKS

5G architecture using nanocore involve the technologies such as cloud computing, nanotechnology, and All IP network. Multiple Access technologies like CDMA, OFDMA, TDMA and FDMA are defined. By increasing the capacity of the system BDMA (Beam Division Multiple Access) technique allows multiple access to Mobile Station (MS). Beam Division Multiple Access (BDMA) transference or transmission provides concurrently numerous users completely over the discrete beams. By organizing out users within the non-overlapping beams, the MU-Multiple input Multiple output channels can be uniformly decay into multiple single-user and Multiple input Multiple output (MIMO) channels; this method significantly reduce above channel estimation method, as well as the processing complexity at transmitters and receivers. Keywords- MIMO, BDMA, nanocore, AIP

ML-Based Massive MIMO Channel Prediction: Does It Work on Real-World Data?

Accurate channel state information (CSI) acquisition is hindered by CSI estimation errors, compression, feedback, and processing delays. We propose a machine learning (ML)-based massive multiple-input multiple-output (mMIMO) channel predictor (CP), which can work on the estimated channel and the compressed version of the estimated channel as well. While existing work has evaluated the performance of ML algorithms by only using the artificially generated channel realizations, this letter reports the results of the ML algorithm using the real-world channel realizations from a measurement campaign performed at Nokia Bell-Labs. The results corroborate the validity of the proposed ML-based CP.

Flipped superposed constellation design for MIMO visible-light communication systems

Multiple-input multiple-output (MIMO) visible-light communication (VLC) systems based on the concept of superposed constellation have attracted increasing attention because multiplexing gains can be achieved even in a highly correlated MIMO channel. In this paper, a novel superposed constellation scheme is proposed for 2×2 MIMO VLC systems, where a 2n-order quadrature amplitude modulation (QAM) signal is obtained by superposing 4QAM and processed 2n-2-order QAM signals. Based on the original 2n-2-order QAM signal, the processed signal is generated by adding an optimal offset, power normalization, and flipping according to the value of the 4QAM signal. Thus, the required power ratio between the two superposed signals is equal to 1, which not only avoids the power competition in the receiver, but also reduces the risk of the nonlinear distortion of the light-emitting diode at the transmitter. Moreover, benefitting from the flipped superposition method, two additional performance gains are provided. First, thorough Gray coding can be achieved. Second, the received power can be improved because the two transmitted signals are correlated. An algorithm for solving the optimal offset is proposed based on the target of the equal power ratio, and the expression of the received power is derived as well. Then, the performance of the proposed scheme is investigated using detailed simulations in an additive white Gaussian noise channel, where the superposed 64QAM constellation is assumed as an example. Further, we perform an experimental demonstration and examine the performance of the proposed system when a practical optical channel is considered, where nonideal impacts, such as nonlinearity and power competition, may occur. The experimental results confirm that the proposed scheme achieves a lower bit error rate (BER) and a larger dynamic range of driving peak-to-peak voltage compared with the existing superposed constellation schemes. Considering the 7% pre-forward error correction BER threshold of 3.8 × 10-3, the proposed superposed 64QAM constellation system can achieve a maximum transmission rate of 3 Gb/s.

Outage capacity analysis of the massive MIMO diversity channel

We consider the massive Multiple Input Multiple Output (MIMO) channel affected by independent and identically distributed Rayleigh fading, with linear processing at both transmitter and receiver sides to pursue full diversity, and analyze its outage capacity for large number of antennas. We first discuss the classical Single Input Multiple Output (SIMO) diversity channel that encompasses Maximal Ratio Combining (MRC) or Selection Combining (SC). For MRC, a numerical computation and a Gaussian Approximation (GA) are considered, whereas for SC an exact evaluation is presented. The analysis is then straightforwardly extended to the Multiple Input Single Output (MISO) diversity channel that encompasses Maximal Ratio Transmission (MRT) or transmit antenna selection. The general full diversity MIMO channel is finally considered, with optimal linear processing or simple antenna selection at both transmitter and receiver. If the number of antennas is sufficiently large on at least one side, the outage capacity of each considered diversity channel approaches that of a reference Additive White Gaussian Noise (AWGN) channel with properly defined Signal-to-Noise Ratio (SNR), which provides a performance benchmark. This conclusion is valid for large but realistic number of antennas compatible with the assumption of independent fading.

Equivalent MIMO Channel Matrix Sparsification for Enhancement of Sensor Capabilities

One of the development directions of new-generation mobile communications is using multiple-input multiple-output (MIMO) channels with a large number of antennas. This requires the development and utilization of new approaches to signal detection in MIMO channels, since the difference in the energy efficiency and the complexity between the optimal maximum likelihood algorithm and simpler linear algorithms become very large. The goal of the presented study is the development of a method for transforming a MIMO channel into a model based on a sparse matrix with a limited number of non-zero elements in a row. It was shown that the MIMO channel can be represented in the form of a Markov process. Hence, it becomes possible to use simple iterative MIMO demodulation algorithms such as message-passing algorithms (MPAs) and Turbo.

A Novel 3D Non-Stationary Maritime Wireless Channel Model

In this paper, a novel 3-dimensional (3D) non-stationary geometry-based stochastic model (GBSM) is proposed to mimic the ship-to-ship multiple-input multiple-output (MIMO) communication channels. To reflect the realistic maritime propagation environment, the effects of rough sea surface scattering and evaporation duct propagation are investigated in the proposed channel model. The model considers the movements of transmitter (Tx), receiver (Rx), and scatterers, and is capable of capturing the channel characteristics, including non-stationary characteristics, spatial consistency, location-dependent property, etc. Through taking the array cluster evolution into account, the proposed channel model can describe massive MIMO channels and can easily switch to conventional MIMO channel model by adjusting corresponding parameters. Based on the proposed model, key statistical properties like delay/angular/Doppler power spectrum density (PSD), space-time correlation function (STCF), stationary interval, and root mean square (RMS) Doppler/delay spreads in multi-scenarios are derived. The usefulness and accuracy of the proposed model are demonstrated by comparing theoretical results, simulation results, and corresponding measurement results.

ChannelGAN: Deep Learning-Based Channel Modeling and Generating

The increasing complexity on channel modeling and the cost on collecting plenty of high-quality wireless channel data have become the main bottlenecks of developing deep learning (DL) based wireless communications. In this letter, a DL-based channel modeling and generating approach namely ChannelGAN is proposed. Specifically, the ChannelGAN is designed on a small set of 3rd generation partnerships project (3GPP) link-level multiple-input multiple-output (MIMO) channel. Moreover, two evaluation mechanisms including i) power comparison from the perspective of delay and antenna domain and ii) cross validation are implemented where the power comparison proves the consistency between the modeled fake channel and real channel, and the cross validation verifies the effectiveness and availability of the generated fake channel for supporting related DL-based channel state information (CSI) feedback.

Ultra-High Mobility Analysis of MIMO Wireless System in Tunnel Scenarios

This letter studies the effect of ultra-high mobility on the performance of multiple-input multiple-output (MIMO) wireless communication system in tunnel scenarios. It is found that the Doppler frequency shift generated by the ultra-high mobility reduces the correlation coefficient. Importantly, it is observed that there exists a key inflection point distance in the ultra-high speed tunnel scene. When this distance is exceeded, the channel correlation of MIMO can be significantly reduced. Moreover, it is perceived that the inflection point distance is relatively stable and is less affected by the communication frequency. On this basis, we propose a high-quality communication area with low MIMO channel correlation. For the ultra-high speed of 720km/h, the inflection distance is about 95m, and the reduction of MIMO channel correlation at 3.5GHz and 28GHz can reach up to 52.7% and 41.2%, respectively. The findings are extremely useful for the applications of the mobile MIMO communication system in tunnel and railway scenarios.

Statistical intrusion detection and eavesdropping in quantum channels with coupling: multiple-preparation and single-preparation methods

Classical, i.e. non-quantum, communications include configurations with multiple-input multiple-output (MIMO) channels. Some associated signal processing tasks consider these channels in a symmetric way, i.e. by assigning the same role to all channel inputs, and similarly to all channel outputs. These tasks especially include channel identification/estimation and channel equalization, tightly connected with source separation. Their most challenging version is the blind one, i.e. when the receivers have (almost) no prior knowledge about the emitted signals. Other signal processing tasks consider classical communication channels in an asymmetric way. This especially includes the situation when data are sent by Emitter 1 to Receiver 1 through a main channel, and an "intruder" (including Receiver 2) interferes with that channel so as to extract information, thus performing so-called eavesdropping, while Receiver 1 may aim at detecting that intrusion. Part of the above processing tasks have been extended to quantum channels, including those that have several quantum bits (qubits) at their input and output. For such quantum channels, beyond previously reported work for symmetric scenarios, we here address asymmetric (blind and non-blind) ones, with emphasis on intrusion detection and additional comments about eavesdropping. To develop fundamental concepts, we first consider channels with exchange coupling as a toy model. We especially use the general quantum information processing framework that we recently developed, to derive new attractive intrusion detection methods based on a single preparation of each state. Finally, we discuss how the proposed methods might be extended, beyond the specific class of channels analyzed here.

A Better Than Alamouti OSTBC for MIMO Backscatter Communications

Backscatter communications have received increasingly attention in Internet of Things (IoT) due to its advantages of requiring no internal battery and almost zero maintenance. However, the drawback of backscatter communications is that, its channel fades deeper than conventional channel. To improve the performance, multiple-input multiple-output (MIMO) techniques have been investigated for backscatter communications. By considering duty cycle, a particular behavior of backscatter communications, as a new performance criterion, we propose a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2 \times 2$ </tex-math></inline-formula> orthogonal space-time block code for MIMO backscatter communications. The new code is a rotation of the classical Alamouti code, and in MIMO backscatter channels, this simple rotation will incur an improved duty cycle for both linear and nonlinear energy harvester models, as well as a same, or even better symbol error rate performance. We provide both rigorous mathematical proofs and numerical simulations to show the above results. An interesting thing is that, this new code does not outperform Alamouti code in conventional MIMO channels, which indicates that the space-time code design criteria for conventional channels is not the optimal one for backscatter channels, and existing space-time codes should be reconsidered and/or redesigned to achieve a higher performance in MIMO backscatter communications.

A High Data Rate Implantable MIMO Antenna for Deep Implanted Biomedical Devices

In this article, a compact multiple-input multiple-output (MIMO) implantable antenna based on meandered resonators is proposed for use in medical implantable devices. Such devices require compactness and high data rates. Regarding compactness, the antenna has a size of <inline-formula> <tex-math notation="LaTeX">$5.35\times 6.2 \times 0.12$ </tex-math></inline-formula> mm³ (operating frequency of 2.45 GHz) which is one of the smallest sizes reported in the literature for this class of antennas. This compactness is achieved using the meandered resonators and slots in the ground plane. For high data rates, the proposed antenna is of MIMO configuration and consists of two probe-fed elements that share the same ground plane and are placed at a distance (edge-to-edge) of 0.4 mm from each other. This configuration reduces multipath distortion and achieves seamless communications with high data rates as evident from conducted link budget analysis and MIMO channel studies. The <i>ex vivo</i> measurements (S-parameters, gain, and radiation patterns) are performed on the proposed antenna when placed inside a capsule and implanted in minced meat. A bandwidth of 320 MHz and isolation of 28 dB are achieved at the design frequency along with omni-directional radiation patterns with a peak realized gain of &#x2212;20.5 dBi. Specific absorption rate (SAR) analysis and input power studies are performed to ensure patient&#x2019;s safety. The result shows that the antenna has a safe 10 g SAR of 402.8 W/kg at an input power of 1 W. Also, the capsule device can be operated at a safe power level of 3.97 mW, which is far greater than the maximum allowable power limit (<inline-formula> <tex-math notation="LaTeX">$25~\mu \text{W}$ </tex-math></inline-formula>).

SLNR-Based Beamspace Precoding and Beam Selection for Wideband mmWave Massive MIMO

Beamspace multiple-input multiple-output (MIMO) systems leverage the lens antenna array to reduce their hardware cost, which is a promising technology in millimeter wave (mmWave) frequency bands. This letter proposes a novel signal-to-leakage-plus-noise ratio (SLNR)-based beamspace precoding and beam selection scheme to improve the downlink sum rate (SR) of the beamspace mmWave massive MIMO channels. First, for a given beam subset, a SLNR maximization-based beamspace precoder is designed to mitigate the inter-user interference caused by leakage power, thus enhancing the SR. Then, an approximated expression of the SR is formulated, based on which an iterative radio frequency (RF) precoding and beam selection method is designed to further improve the system SR. Numerical results demonstrate a superior SR performance of the proposed scheme over state-of-the-art methods.

A Statistical MIMO Channel Model for Reconfigurable Intelligent Surface Assisted Wireless Communications

Reconfigurable intelligent surface (RIS) consisting of a large number of programmable near-passive units has been a hot topic in wireless communications due to its capability in providing smart radio environments to enhance the communication performance. However, the existing research are mainly based on simplistic channel models, which will, in principle, lead to inaccurate analysis of the system performance. In this paper, we propose a general three-dimensional (3D) wideband non-stationary end-to-end channel model for RIS assisted multiple-input multiple-output (MIMO) communications, which takes into account the physical properties of RIS, such as unit numbers, unit sizes, array orientations and array configurations. By modeling the RIS by a virtual cluster, we describe the end-to-end channel by a superposition of virtual line-of-sight (V-LoS), single-bounced non-LoS (SB-NLoS), and double-bounced NLoS (DB-NLoS) components. We also derive an equivalent cascaded channel model and show the equivalence between end-to-end and cascaded modeling of RIS channels. Then, a sub-optimal solution with low complexity is used to derive the RIS reflection phases. The impact of physical properties of RIS, such as unit numbers, unit sizes, array orientations, array configurations and array relative locations, on channel statistical characteristics has been investigated and analyzed, the results demonstrate that the proposed model is helpful for characterizing the RIS-assisted communication channels.

A stochastic confocal ellipsoid channel model for high speed railway MIMO communication systems

This paper investigates the channel model for multiple-input multiple-output (MIMO) communication systems in high speed railway (HSR) networks. Specially, a three-dimensional (3D) confocal stochastic geometry ellipsoid model is proposed for modeling the MIMO channels. Normalized space–time correlation function(ST CF), Spatial cross correlation function (CCF) and level crossing rate (LCR) of both theoretical and simulation model are have been derived and analyzed. As a 3D GBSM, the proposed confocal ellipsoid model has lower correlation in spatial cross-correlation function (CCF) compared with that of the corresponding two-dimensional (2D) ellipse model. Measurement data of different HSR scenarios verify the applicability of the confocal ellipsoid model.

Performance Analysis of Channel Estimation for Massive MIMO Communication Using DL-Based Fully Connected Neural Network (DL-FCNN) Architecture

The latest research for applying deep learning in wireless communications gives several opportunities to reduce complex signal processing. The channel estimation is important to study the nature of the varying channel and to calculate channel state information (CSI) value which is utilized at the receiver to nullify the interference which occurs during multipath transmission. In the current article, considering the massive Multiple Input Multiple Output (MIMO) channel model, a DL approach is developed with a fully connected neural network (NN) architecture which is used to estimate the channel with minimum error. The proposed DL architecture uses an openly available channel dataset. Further, using generated pilot symbols of lengths 2 and 4, the performance of DL-based Fully connected NN (DL-FCNN) is analyzed to estimate the channel in uplink massive MIMO communication. The obtained results demonstrate that the channel estimation performance was calculated in terms of normalized mean square error((NMSE) for different values of SNR added at receiver base station (BS) to the signals over the range of BS antennas. Also, the channel estimation error over a large number of BS antennas for massive MIMO scenarios is observed, and it is observed that the NMSE reduces with a greater number of antennas. Hence, it can be inferred that the DL models will be the future for most physical layer signal processing techniques such as channel estimation, modulation detection, etc. within massive MIMO networks.

Joint Synchronization and Compressive Channel Estimation for Frequency-Selective Hybrid mmWave MIMO Systems

Synchronization is a fundamental procedure in cellular systems whereby user equipment (UE) acquires the time and frequency information required to decode the data transmitted by a base station (BS). Due to the small antenna aperture at millimeter wave (mmWave), large antenna arrays are used to compensate for the low signal-to-noise ratio (SNR) at the receiver. For this reason, synchronization is often performed jointly with beam training as in 5G New Radio (NR) to boost the receive SNR. However, if the mmWave multiple-input multiple-output (MIMO) channel is to be estimated, then synchronization must be performed in the low SNR regime owing to the lack of directional beamforming during compressive channel estimation. Current mmWave channel estimation algorithms implicitly take synchronization for granted, which is a not practical assumption in the low SNR regime. To solve this problem, this work proposes the first synchronization framework for hybrid mmWave MIMO that is robust to carrier frequency offset (CFO) and phase noise (PHN) synchronization errors. We propose two novel multi-stage algorithms to jointly estimate the various unknown parameters, based on the expectation-maximization (EM) algorithm. By using the QuaDRiGa 5G NR channel simulator, we show that compressive channel estimation without prior synchronization is possible, and the proposed approaches outperform current solutions for joint beam training and synchronization currently considered in 5G NR.

Simultaneous Learning and Inferencing of DNN-Based mmWave Massive MIMO Channel Estimation in IoT Systems With Unknown Nonlinear Distortion

In this article, we propose an online training framework for deep neural network (DNN)-based mmWave massive multiple-input multiple-output (MIMO) channel estimation (CE) in Internet-of-Things (IoT) systems with nonlinear amplifier distortions. The DNN-based channel estimator is trained online in the IoT device based on real-time received pilot measurements from the base station (BS) without knowledge of the true channels, and can simultaneously generate CE in real time. To realize this, we first propose three axioms for a legitimate online loss function under known nonlinearity, based on which we develop a channel model-free online training algorithm with convergence analysis. For unknown nonlinearity, we propose a two-stage DNN structure with nonlinear modules, for which the DNN-based CE and nonlinear functions can be jointly trained online based on real-time received pilots. Simulation results show that the proposed solution achieves better CE accuracy than traditional compressive sensing (CS) algorithms while enjoying a much faster computational efficiency. In addition, the proposed method is robust to various nonlinear channel model mismatches and is able to track the change of the nonlinear channel model.