Channel Impulse Response Research Articles

B-Spline Neural Network Assisted Space-Time Equalization for Single-Carrier Multiuser Nonlinear Frequency-Selective MIMO Uplink

This paper designs a nonlinear space-time equalizer based on B-spline neural network (BSNN) for the singlecarrier high-throughput multiuser frequency-selective multipleinput multiple-output (MIMO) nonlinear uplink. Specifically, based on a BSNN parametrization of the nonlinear high power amplifiers (NHPAs) at mobile terminals’ transmitters, a novel nonlinear identification scheme is developed to estimate the nonlinear dispersive MIMO uplink channel, which includes the BSNN models for the NHPAs at transmitters as well as the frequency-selective MIMO channel impulse response (CIR) matrix. Furthermore, the BSNN inverse models of the NHPAs are also estimated in closed-form. This allows the base station to implement nonlinear multiuser detection effectively using the space-time equalization (STE) based on the estimated frequencyselective MIMO CIR matrix and followed by compensating for the nonlinear distortion of the transmitters’ NHPAs based on the estimated BSNN inverse models. Simulation results are utilized to demonstrate the superior bit error rate performance of our nonlinear STE approach for single-carrier highthroughput multiuser nonlinear frequency-selective MIMO uplink.

Ray-based blind deconvolution with maximum kurtosis phase correction.

Ray-based blind deconvolution (RBD) is a method that estimates the source waveform and channel impulse response (CIR) using the ray arrival in an underwater environment. The RBD estimates the phase of the source waveform by using beamforming. However, low sampling, array shape deformation, and other factors can cause phase errors in the beamforming results. In this paper, phase correction is applied to the beamforming estimated source phase to improve RBD performance. The impulsiveness of the CIR was used as additional information to correct the initially estimated source phase. Kurtosis was used to measure impulsiveness, and the phase correction that maximized the kurtosis of the CIRs was calculated through optimization. The proposed approach is called ray-based blind deconvolution with maximum kurtosis phase correction (RBD-MKPC) and is based on a single-input multiple-output system. The RBD-MKPC was tested with several CIRs and source waveform combinations in the shallow-water acoustic variability experiment 2015 using broadband high-frequency pulses (11-31 kHz) as the source and a sparse vertical 16-element line array as receivers. The results indicate that the RBD-MKPC improves the estimation performance. In addition, from an optimization point of view and compared with other initialization methods, the proposed method showed superior convergence speed and estimation performance.

Experimental System for Molecular Communication in Pipe Flow With Magnetic Nanoparticles

In the emerging field of molecular communication (MC), testbeds are needed to validate theoretical concepts, motivate applications, and guide further modeling efforts. To this end, this paper presents a flexible and extendable in-vessel testbed for flow-based macroscopic MC, abstractly modeling, e.g., a part of a chemical reactor or a blood vessel. Signaling is based on injecting non-reactive superparamagnetic iron oxide nanoparticles (SPIONs) dispersed in an aqueous suspension into a tube with background flow. A commercial magnetic susceptometer is used for non-intrusive downstream signal reception. To shed light on the operation of the testbed, we identify the physical mechanisms governing the transmission, propagation, and reception of the information-carrying SPIONs. Moreover, to facilitate system design, we propose a closed-form parametric expression for the end-to-end channel impulse response (CIR). The proposed CIR model is shown to consistently capture the experimentally observed distance-dependent impulse response peak heights and peak decays for transmission distances from 5cm to 40cm. Moreover, to validate our testbed, reliable communication is demonstrated based on experimental data for model-agnostic and model-based detection methods.

Machine Learning-Based Angle of Arrival Estimation for Ultra-Wide Band Radios

This letter analyzes the feasibility of deep convolutional neural networks (DCNN) for accurate ultra-wideband (UWB) angle of arrival estimation that is robust against hardware imperfections. To this end, a uniform linear array with four antenna elements is leveraged and a DCNN approach is proposed and compared with traditional approaches, such as MUSIC and phase difference of arrival estimators, for different environments, number of available channel impulse responses, and polarization mismatches, in terms of absolute value of error and computational complexity. The proposed approach outperforms the traditional approaches up to 80° error reduction at a computational complexity increase of only 10% compared to MUSIC.

A Learning-Based AoA Estimation Method for Device-Free Localization

Device-free localization (DFL), an important aspect in integrated sensing and communication, can be achieved through exploiting multipath components in ultra-wide bandwidth systems. However, incorrect identification of multipath components in the channel impulse responses will lead to large angle-of-arrival (AoA) estimation errors and subsequently poor localization performance. This letter proposes a learning-based AoA estimation method to improve the DFL accuracy. In the proposed method, we first design a classifier to identify the multipath components and then exploit the phase-difference-of-arrival to mitigate the AoA estimation error through a multilayer perceptron. Our learning-based method is validated using the datasets collected by ultra-wide bandwidth arrays, which significantly outperforms conventional methods in terms of AoA estimation and localization performance.

Enhanced feature selection method based on regularization and kernel trick for 5G applications and beyond

Prediction of wireless channel scenarios is fundamental for modern wireless communication systems with diverse propagation conditions. Moreover, the type of data extracted from a wireless communication channel impulse response (CIR) is complex. In recent research, machine learning (ML) techniques have proven their success in classification problems of wireless communication scenarios and provide reasonable results. In this paper, a new enhanced feature selection method is proposed to improve the training model and classification performance of the conventional model. This improvement is achieved based on the concept of regularization in which the selection of the best features is considered before training the model under any propagation environment. The adoption of regularization leads to a high Total Explained Variance (TEV) during the process of kernel Principal Component Analysis (k-PCA). As a consequence, two principal features are used instead of three. The proposed model has high generalization ability since it reduces the features dimensionality (computational complexity) and, generally, enhances the ML classification performance. Experimental simulation is executed to compare the proposed model and the conventional one in terms of accuracy, precision and recall. The accuracy is increased from 97% to 99%, from 96% to 99%, from 89% to 97% and from 90% to 98% for k-nearest neighbor (k-NN), support vector machine (SVM), k-Means and Gaussian mixture model (GMM), respectively.

Channel Characterization of Diffusion-Based Molecular Communication With Multiple Fully-Absorbing Receivers

In this paper an analytical model is introduced to describe the impulse response of the diffusive channel between a pointwise transmitter and a given fully-absorbing (FA) receiver in a molecular communication (MC) system. The presence of neighbouring FA nanomachines in the environment is taken into account by describing them as sources of negative molecules. The channel impulse responses of all the receivers are linked in a system of integral equations. The solution of the system with two receivers is obtained analytically. For a higher number of receivers the system of integral equations is solved numerically. It is also shown that the channel impulse response shape is distorted by the presence of the neighbouring FA interferers. For instance, there is a time shift of the peak in the number of absorbed molecules compared to the case without interference, as predicted by the proposed model. The analytical derivations are validated by means of particle based simulations.

Time‐Varying Channel Estimation Based on Air‐Ground Channel Modelling and Modulated Learning Networks

To improve the time-varying channel estimation accuracy of orthogonal frequency division multiplexing air-ground datalink in complex environment, this paper proposes a time-varying air-ground channel estimation algorithm based on the modulated learning networks, termed as MB-ChanEst-TV. The algorithm integrates the modulated convolutional neural networks (MCNN) with the bidirectional long short term memory (Bi-LSTM), where the MCNN subnetworks accomplish channel interpolation in frequency domain and compress the network model while the Bi-LSTM subnetworks achieve channel prediction in time domain. Considering the unique characteristics of airframe shadowing for unmanned aircraft systems, we propose to combine the classical 2-ray channel model with the tapped delay line model and present a more realistic channel impulse response samples generation approach, whose code and dataset have been made publicly available. We demonstrate the effectiveness of our proposed approach on the generated dataset, where experimental results indicate that the MB-ChanEst-TV model outperforms existing state-of-the-art methods with a lower estimation error and better bit error ratio performance under different signal to noise ratio conditions. We also analyze the effect of roll angle of the aircraft and the duration percentage of the airframe shadow on the channel estimation.

NLOS identification using parallel deep learning model and time-frequency information in UWB-based positioning system

Ultra wide band (UWB) radio positioning technology is widely used in indoor high-precision positioning scenes, while obstacles in the wireless signal propagation path will cause the non-line of sight (NLOS) propagation of UWB signal and the reduction of positioning reliability. In this paper, an efficient NLOS identification scheme based on multiple input learning (MIL) neural network model with channel impulse response (CIR) and time-frequency diagram of CIR (TFDOCIR) is proposed by direct detection in UWB positioning system. It is experimentally demonstrated that the average NLOS identification accuracies reach 86.82%, 92.53%, 91.61%, 92.91%, 92.02% corresponding to five different obstacles including wooden door, concrete wall, metal plate, human body and glass window, respectively. Additionally, the overall NLOS identification accuracy achieves 91.74%. Through the proposed NLOS identification scheme with weight least squares (WLS), the indoor UWB-based positioning tests are performed with the average error 7.35 cm, thereby proving its ability of ranging error preventation.

Research on indoor positioning of power grid equipment based on deep learning

In view of the increasing number of power grid equipment and a large number of indoor deployments, the existing positioning technology cannot meet the requirements. In this paper, a DOA estimation method based on deep learning is proposed to improve the indoor positioning accuracy. Firstly, by studying the propagation characteristics of wireless signals and the transmission characteristics of indoor wireless channels, a statistical channel model with multi subcarrier characteristics is established to generate channel impulse response information. Furthermore, the transformation matrix required for virtual antenna expansion is designed by using the idea of interpolation transformation. This matrix is combined with the channel impulse response to obtain the virtual antenna array data for DOA estimation. The DOA estimation model based on CNN is optimized to obtain high-precision DOA. Finally, the simulation results show that the positioning accuracy can be doubled by using the virtual antenna array technology. The performance of the two-stage DOA estimation algorithm proposed in this paper is slightly better than the accuracy of a large difference step. Even when there are fewer APS, the positioning accuracy is better than the traditional MUSIC algorithm, which is more suitable for indoor positioning of a large number of power grid equipment in the future.

Multipath-Assisted Radio Sensing and State Detection for the Connected Aircraft Cabin.

Efficiency and reliable turnaround time are core features of modern aircraft transportation and key to its future sustainability. Given the connected aircraft cabin, the deployment of digitized and interconnected sensors, devices and passengers provides comprehensive state detection within the cabin. More specifically, passenger localization and occupancy detection can be monitored using location-aware communication systems, also known as wireless sensor networks. These multi-purpose communication systems serve a variety of capabilities, ranging from passenger convenience communication services, over crew member devices, to maintenance planning. In addition, radio-based sensing enables an efficient sensory basis for state monitoring; e.g., passive seat occupancy detection. Within the scope of the connected aircraft cabin, this article presents a multipath-assisted radio sensing (MARS) approach using the propagation information of transmitted signals, which are provided by the channel impulse response (CIR) of the wireless communication channel. By performing a geometrical mapping of the CIR, reflection sources are revealed, and the occupancy state can be derived. For this task, both probabilistic filtering and k-nearest neighbor classification are discussed. In order to evaluate the proposed methods, passenger occupancy detection and state detection for the future automation of passenger safety announcements and checks are addressed. Therefore, experimental measurements are performed using commercially available wideband communication devices, both in close to ideal conditions in an RF anechoic chamber and a cabin seat mockup. In both environments, a reliable radio sensing state detection was achieved. In conclusion, this paper provides a basis for the future integration of energy and spectrally efficient joint communication and sensing radio systems within the connected aircraft cabin.

UWB LOS/NLOS identification in multiple indoor environments using deep learning methods

Indoor location-aware service is booming in daily life and business activities, making the demand for precise indoor positioning systems thrive. The identification between line-of-sight (LOS) and non-line-of-sight (NLOS) is critical for wireless indoor time-of-arrival-based localization methods. Ultra-Wide-Band (UWB) is considered low cost among the many wireless positioning systems. It can resolve multi-path and have high penetration ability. This contribution addresses UWB NLOS/LOS identification problem in multiple environments. We propose a LOS/NLOS identification method using Convolutional Neural Network parallel with Gate Recurrent Unit, named Indoor NLOS/LOS identification Neural Network. The Convolutional Neural Network extracts spatial features of UWB channel impulse response data. While the Gate Recurrent Unit is an effective approach for designing deep recurrent neural networks which can extract temporal features. By integrating squeeze-and-extraction blocks into these architectures we can assign weights on channel-wise features. We simulated UWB channel impulse response signals in residential, office, and industrial scenarios based on the IEEE 802.15.4a channel model report. The presented network was tested in simulation scenarios and an open-source real-time measured dataset. Our method can solve NLOS identification problems for multiple indoor environments. Thus more versatile compare with networks only working in one scenario. Popular machine learning methods and deep learning methods are compared against our method. The test results show that the proposed network outperforms benchmark methods in simulation datasets and real-time measured datasets.

Improvement of ray-based blind deconvolution through kurtosis-based phase correction

Ray-based blind deconvolution (RBD) has demonstrated remarkable performance on several in situ underwater acoustic data. RBD estimates the channel impulse response by compensating the source phase estimated by beamforming. However, when beamforming fails to achieve the desired signal-to-noise ratio, RBD fails to achieve the expected performance. To address this issue, we propose an improved RBD framework that uses the kurtosis of the channel impulse response to provide additional phase correction after beamforming. The proposed method is validated with the measurement data from the shallow-water acoustic variability experiment 2015 using a high-frequency (11–31 kHz) broadband source and a vertical line array of 16 sensors located approximately 3 km away. The results are compared with conventional RBD and discussed from an optimization point of view.

Reflection-Assisted Non-Line-of-Sight Ultraviolet Communications

Considering high path loss (PL) of the non-line-of-sight (NLOS) ultraviolet communications (UVC) realized by the atmospheric scattering effect of ultraviolet light, we propose to use a reflective plane to improve the received optical power and further reduce the PL. More specifically, a channel model for NLOS UVC with not only scattering but also reflection considered is proposed via Monte-Carlo (MC) photon-tracing method. Both scattering and reflective events are called collision-induced events (CIEs). The proposed channel model can obtain the PL and channel impulse response (CIR) performances of the NLOS UVC system with multiple-order CIEs. It is shown that the reflective plane could reduce the PL and the time delay of CIR, which could be a benefit for designing high data-rate NLOS UVC systems. Finally, experimental results in two representative scenarios are also given to verify the effectiveness of the proposed channel model for reflection-assisted NLOS UVC systems.

Relative Localization for Silent Absorbing Target in Diffusive Molecular Communication System

Recently, anomaly detection or localization in molecular communication (MC) system has become a research hot topic that can be used to distinguish the abnormal cells in the human body. Due to no or very few molecules the target released, it is difficult to detect the silent target in the MC system. In this article, we construct a three-node MC (TNMC) system, including a point source transmitter, a passive receiver, and a silent absorbing target (SAT), the SAT cannot release but has the ability to absorb the molecules. Based on the above system, we propose a probability equivalent modeling (PEM) method to model the channel of the TNMC system and derive the channel impulse response (CIR) of the receiver. Besides, we propose a localization algorithm based on maximum-likelihood estimation (MLE) and Newton–Raphson method to spot the relative location of the SAT. Simulation results verify the correctness of the PEM method and effectiveness for localizing the target.

Multistatic UWB Radar-Based Passive Human Tracking Using COTS Devices

Due to its high delay resolution, the ultra-wideband (UWB) technique has been widely adopted for fine-grained indoor localization. Instead of active positioning, UWB radar-based passive human tracking is explored using commercial off-the-shelf (COTS) devices. To extract the time-of-flight (ToF) reflected by the moving person, the accumulated channel impulse responses (CIR) and the corresponding variances are used to train the convolutional neural networks (CNN) model. Particle filter algorithm is adopted to track the moving person based on the extracted ToFs of all pairs of links. Experimental results show that the proposed CIR- and variance-based CNN models achieve less than 30-cm root-mean-square errors (RMSEs). Especially, the variance-based CNN model is robust to the scenario changing and promising for practical applications.

Non-line of sight visible light communications: A technical and application based survey

Visible Light Communication (VLC) do not suffer from electromagnetic interference and can be safely used in sensitive environments such as chemical plants, aircrafts and hospitals, as well as in high vehicle density VLC systems advantages of have high speed, greater power efficiency and a high spatial reuse ratio makes it a promising candidate for object allocation detection and V2V communication. This work review the need to assess the Non-Line of Sight propagation in VLC systems as the real applications have Line of Sight (LoS) and Non Line of Sight (NLoS) transmission. The challenges in VLC development and implementation are highlighted, and channel modelling of the NLoS VLC systems to ensure accuracy of channel capacity is discussed. Optimizing the configurations of VLC networks is important to overcome the challenges in VLC implementation and channel modelling. MIMO configurations in NLoS VLC systems coupled with suitable equalization techniques may be required to mitigate the high interference. BER, SNR and channel impulse response are some parameters that should be investigated to assess the performance of the NLoS VLC system.

A Photolysis-Assist Molecular Communication for Tumor Biosensing.

Molecular communication (MC) is a promising bioinspired paradigm for exchanging molecule information among nanomachines. In this paper, we propose a synchronization-assist photolysis MC system that aims to transmit the biosensing signal of the tumor microenvironment, facilitated by mitigating redundant molecules for improved bit error rate (BER) performance. Benefits from biocompatible MC, biosensors could transmit biosensing signals of the tumor in vivo instead of converting them to electrical signals. Due to diffusion motion’s slow and stochastic nature, intersymbol interference (ISI), resulting from previous symbols’ residual information molecules, inevitably occurs in diffusion-based MC. ISI is one of the challenges in diffusion-based MC, which significantly impacts signal detection. Inspired by on–off keying (OOK) modulation, the proposed modulation implements a switch of molecules and light alternatively. The light emitted is triggered by a synchronization signal, and the photolysis reactions could reduce the redundant molecules. An expression for the relevant channel impulse response (CIR) is derived from a hybrid channel model of diffusion and photolysis reaction. In this paper, we implement the maximum posterior estimation scheme to find the optimal decision threshold and analysis the BER performance in terms of different time intervals of the system. Numerical simulations demonstrate that the proposed method can improve the channel capacity and BER performance. We believe that our work may pave the way for MC application in biosensing.

An Enhanced VLC Channel Model for Underground Mining Environments Considering a 3D Dust Particle Distribution Model.

Underground Mining (UM) is a hostile industry that generally requires a wireless communication system as a cross-cutting axis for its optimal operation. Therefore, in the last five years, it has been shown that, in addition to radio-frequency-based communication links, wireless optical communications, such as Visible Light Communication (VLC), can be applied to UM environments. The application of VLC systems in underground mines, known as UM-VLC, must take into account the unique physical features of underground mines. Among the physical phenomena found in underground mines, the most important ones are the positioning of optical transmitters and receivers, irregular walls, shadowing, and a typical phenomenon found in tunnels known as scattering, which is caused by the atmosphere and dust particles. Consequently, it is necessary to use proper dust particle distribution models consistent with these scenarios to describe the scattering phenomenon in a coherent way in order to design realistic UM-VLC systems with better performance. Therefore, in this article, we present an in-depth study of the interaction of optical links with dust particles suspended in the UM environment and the atmosphere. In addition, we analytically derived a hemispherical 3D dust particle distribution model, along with its main statistical parameters. This analysis allows to develop a more realistic scattering channel component and presents an enhanced UM-VLC channel model. The performance of the proposed UM-VLC system is evaluated using computational numerical simulations following the IEEE 802.1.5.7 standard in terms of Channel Impulse Response (CIR), received power, Signal-to-Noise-Ratio (SNR), Root Mean Square (RMS) delay spread, and Bit Error Rate (BER). The results demonstrate that the hemispherical dust particle distribution model is more accurate and realistic in terms of the metrics evaluated compared to other models found in the literature. Furthermore, the performance of the UM-VLC system is negatively affected when the number of dust particles suspended in the environment increases.

Estimating TOA Reliability With Variational Autoencoders

Radio frequency (RF)-based localization yields centimeter-accurate positions under mild propagation conditions. However, propagation conditions predominant in indoor environments (e.g. industrial production) are often challenging as signal blockage, diffraction and dense multipath lead to errors in the time of flight (TOF) estimation and hence to a degraded localization accuracy. A major topic in high-precision RF-based localization is the identification of such anomalous signals that negatively affect the localization performance, and to mitigate the errors introduced by them. As such signal and error characteristics depend on the environment, data-driven approaches have shown to be promising. However, there is a trade-off to a bad generalization and a need for an extensive and time-consuming recording of training data associated with it. We propose to use generative deep learning models for out-of-distribution detection based on channel impulse responses (CIRs). We use a Variational Autoencoder (VAE) to predict an anomaly score for the channel of a TOF-based Ultra-wideband (UWB) system. Our experiments show that a VAE trained only on line-of-sight (LOS) training data generalizes well to new environments and detects non-line-of-sight CIRs with an accuracy of 85%. We also show that integrating our anomaly score into a TOF-based extended Kalman filter (EKF) improves tracking performance by over 25%.