Time-varying Channel Impulse Response Research Articles

Detecting Weak Underwater Targets Using Block Updating of Sparse and Structured Channel Impulse Responses

In this paper, we considered the real-time modeling of an underwater channel impulse response (CIR), exploiting the inherent structure and sparsity of such channels. Building on the recent development in the modeling of acoustic channels using a Kronecker structure, we approximated the CIR using a structured and sparse model, allowing for a computationally efficient sparse block-updating algorithm, which can track the time-varying CIR even in low signal-to-noise ratio (SNR) scenarios. The algorithm employs a conjugate gradient formulation, which enables a gradual refinement if the SNR is sufficiently high to allow for this. This was performed by gradually relaxing the assumed Kronecker structure, as well as the sparsity assumptions, if possible. The estimated CIR was further used to form a residual signal containing (primarily) information of the time-varying signal responses, thereby allowing for the detection of weak target signals. The proposed method was evaluated using both simulated and measured underwater signals, clearly illustrating the better performance of the proposed method.

Evaluation of Non-Coherent Signal Detection Techniques for Mobile Molecular Communication.

In recent years, there have been more and more research on molecular communication (MC). Because the deployment of mobile nanomachines may be required in some applications of MC, research on mobile MC has become a trend. The signal detection schemes for static MC are no longer applicable due to the time varying channel impulse response (IR), which is caused by the mobile characteristics of nanomachines. In this paper, a low complexity and non-coherent detection scheme is proposed for mobile scenario, which is based on the energy difference between two adjacent symbols. Most of the existing signal detection methods do not consider inter-symbol interference (ISI). Compared with those methods, the proposed scheme can achieve signal detection utilizing ISI without knowing channel state information (CSI). The bit error rate (BER) performance of the proposed method is investigated under different conditions through simulations. Besides, the influence of mobility features of nanomachines on the signal detection accuracy is also investigated in detail. The simulation results demonstrate that the BER performance of the proposed scheme outperforms the latest signal detection scheme for short-distance mobile MC system with high velocity. Consequently, the detection scheme proposed in this paper can reduce the influence of nanomachines' mobility and has the potential to be used in mobile MC systems.

Ray tracing for modeling underwater acoustic communications channel between moving platforms, with moving ocean surface and changing bathymetry

Underwater acoustic communication systems must contend with a time-varying channel due to motion of source, receiver, point of reflection from ocean surface waves, and point of reflection from the seabed over changing bathymetry. These motions produce Doppler effects, which impact acomms systems. The signal processing to equalize such channel fading typically consists of a number of tracking loops, whose testing requires time varying realizations of such channels. A realistic channel model would enable improved modem designs and hardware-in-the-loop testing. We will focus on how to handle the ocean surface and the changing bathymetry. We propose an architecture which pre-computes the ray tracing for the part of the ocean that is not changing, then updates only small subsets of the ocean where dynamics occur. This consists of identifying the point of specular reflection, then interpolating the source-to-reflector and receiver-to-reflector rays to produce the union of these two paths. Such acoustic paths may be shadowed by the shape of the ocean surface. We will present examples of a time-varying channel impulse response, using the combined travel times, and discuss how dynamic ray tracing can be used to produce the amplitude of the joined paths.

Time-variance of 60 GHz vehicular infrastructure-to-infrastructure (I2I) channel

In this paper, we study the time-variance of a roadside infrastructure to infrastructure (I2I) channel operating at 60 GHz millimetre wave (mmWave) band, where the time-variance is caused by moving vehicles acting as scatterers. At first, measurement data is obtained by placing the transmitter (TX) and the receiver (RX) at different heights to emulate a link between two nonidentical roadside units (RSUs), and time-domain channel sounding is performed by sending complementary Golay sequences from the TX to the RX. A linear piece-wise interpolation of the corresponding temporal auto-correlation function (ACF) is used to find the Doppler spread of the I2I channel, where our interpolation method compensates for a slower sampling rate. Next, a framework is presented for time-variant channel impulse response (CIR) simulation which focuses on moving scatterers only and validates the linear piece-wise ACF model. The framework is useful for time-variant vehicular I2I channel simulation and in speed estimation related vehicular applications. Finally, a double-slope curve-fitted analytical model for ACF is proposed as a generalisation to the linear piece-wise model. The proposed model and its Doppler spectrum are found to be in agreement with the analytical results for fixed-to-fixed (F2F) channels with moving scatterers and matches perfectly with the measured data.

Measurement‐based geometrical characterisation of the vehicle‐to‐vulnerable‐road‐user communication channel

Vehicle-to-vulnerable-road-user (V2VRU) communications have the ability to provide of awareness to both vehicles and vulnerable road users to prevent accidents. An accurate V2VRU channel model in critical accident scenarios is essential to develop a reliable communications system. Therefore, extensive wideband single-input and single-output channel measurement campaigns at 5.2 GHz were carried out in open-field and urban environments. Accident prone scenarios between a vehicle and a cyclist as well as between a vehicle and a pedestrian are considered. In this study, locations of the scatterers in the propagation environment are estimated. The authors propose a method to extract specular multipath components (MPCs) from the estimated time-variant channel impulse response based on the density of neighbouring MPCs. The specular MPCs are then tracked using a novel tracking algorithm based on the multipath component distance approach. Each path is then related to a physical scatterer in the propagation environment by employing a joint delay-Doppler estimation. According to the results, single and double bounce reflections from buildings and parked vehicles are identified in line-of-sight (LoS) situation. In non-LoS(NLoS) situation, scattering from nearby trees as well as reflections from traffic signs and lampposts beneath the trees canopy are identified.

Diffusive Mobile MC With Absorbing Receivers: Stochastic Analysis and Applications

This paper presents a stochastic analysis of the time-variant channel impulse response (CIR) of a three dimensional diffusive mobile molecular communication (MC) system where the transmitter, the absorbing receiver, and the molecules can freely diffuse. In our analysis, we derive the mean, variance, probability density function (PDF), and cumulative distribution function (CDF) of the CIR. We also derive the PDF and CDF of the probability ${p}$ that a released molecule is absorbed at the receiver during a given time period. The obtained analytical results are employed for the design of drug delivery and MC systems with imperfect channel state information. For the first application, we exploit the mean and variance of the CIR to optimize a controlled-release drug delivery system employing a mobile drug carrier. We evaluate the performance of the proposed release design based on the PDF and CDF of the CIR. We demonstrate significant savings in the amount of released drugs compared to a constant-release scheme and reveal the necessity of accounting for the drug-carrier’s mobility to ensure reliable drug delivery. For the second application, we exploit the PDF of the distance between the mobile transceivers and the CDF of ${p}$ to optimize three design parameters of an MC system employing on-off keying modulation and threshold detection. Specifically, we optimize the detection threshold at the receiver, the release profile at the transmitter, and the time duration of a bit frame. We show that the proposed optimal designs can significantly improve the system performance in terms of the bit error rate and the efficiency of molecule usage.

Circular Superposition Spread-Spectrum Transmission for Multiple-Input Multiple-Output Underwater Acoustic Communications

This letter proposes a circular superposition spread spectrum (CSSS) scheme for multiple input-multiple-output (MIMO) underwater acoustic (UWA) communications where the multipath channel length is long and the Doppler spread is severe. The proposed CSSS scheme divides the spreading sequence into several sub-sequences, and the circular shifting of the sub-sequences forms multiple spreading sequences. Each spreading sequence spreads different payload symbols individually, then the multiple spread symbols are summed to form the transmitted signal at one transducer. The CSSS signaling enables the receiver to apply a circularly shifted coherent RAKE to harvest the delay-spatial diversity of the MIMO UWA channels. If one of baseband symbol is known to the receiver, then the known symbol is utilized as the training symbol to estimate the time-variant channel impulse response (CIR) at every sub-sequence length. The receiver also reduces the co-channel interference (CCI) by exploiting the periodical auto-correlation property of the circular spreading sequences. Simulation results demonstrate that the proposed CSSS achieves more robust performance than the conventional direct sequence spread spectrum (DSSS) approach while reducing the training overhead for MIMO systems.

Real-Time Geometry-Based Wireless Channel Emulation

Connected autonomous vehicles and industry 4.0 production scenarios require ultrareliable low-latency communication links. The varying positions of transmitter, reflecting objects, and receiver cause a nonstationary time- and frequency-selective fading process. In this paper, we present the necessary hardware architecture and signal processing algorithms for a real-time geometry-based channel emulator, that is needed for testing of wireless control systems. We partition the nonstationary fading process into a sequence of local stationarity regions and model the channel impulse response as sum of propagation paths with time-varying attenuation, delay, and Doppler shift. We implement a subspace projection of the propagation path parameters, to compress the time-variant channel impulse response. This enables a low data-rate link from the host computer, which computes the geometry-based propagation paths, to the software defined radio unit, that implements the convolution on a field programmable gate array (FPGA). With our new architecture, the complexity of the FPGA implementation becomes independent of the number of propagation paths. Our channel emulator can be parametrized by all known channel models. Without loss of generality, we use a parameterization by a geometry-based stochastic channel model, due to its nonstationary nature. We provide channel impulse response measurements of the channel emulator, using the RUSK Lund channel sounder for a vehicular scenario with 617 propagation paths. A comparison of the time-variant power delay profile and Doppler spectral density of simulated and emulated channel impulse response showed a close match with an error smaller than -35 dB. The results demonstrate that our channel emulator is able to accurately emulate nonstationary fading channels with continuously changing path delays and Doppler shifts.

Blind equalization and automatic modulation classification of underwater acoustic signals

It is useful to passively characterize the underwater acoustic communications environment for a variety of purposes, including interference avoidance and enforcement of restrictions protecting marine mammals. Automatically determining the modulation of a received waveform can permit sonar or communications operations within the same bandwidth with a minimum of collisions, and it can identify a particular system operating outside its permitted regime. The characterization system needs to both determine the modulation and an unknown, time-varying channel impulse response since the transmitter and receiver are not coordinating. In this work, we demonstrate the use of blind equalization along with Convolutional Neural Networks for automatic classification of underwater signals. Our current research focuses on classification of constant modulus signals and demonstrates an approximate 30 percent improvement in modulation classification, compared to approaches without equalization, and a significant reduction in the amount of data needed for training. We considered BPSK, QPSK, MSK, FSK and 8-PSK modulations using simplified synthetic channels simulated via MATLAB to demonstrate our results. Future work is aimed at demonstrating classification improvement using realistic channel models simulated via the Sonar Simulation Toolkit, real underwater channels gathered from data collects, and additional underwater acoustic signal types.

Classification of multiple source depths in a time-varying ocean environment using a convolutional neural network (CNN)

Machine learning (ML) is the idea that there are generic algorithms that can tell us something interesting about a set of data without us having to write any custom code specific to the problem. Instead of writing code, we feed data to the generic algorithm and it builds its own logic based on the data. In this paper, ML is applied to classification of multiple (4) sources at various depths in a shallow-water waveguide where the input data are time-varying channel impulse responses (CIRs) received by a vertical array (4 hydrophones) from each of the sources over a period of 22 hours (528 examples). Specifically, the first 16 hours of CIRs are used for training a 4-layered CNN (convolutional neural net) to exploit the structure of CIRs in the two-dimensional time and depth domain, similar to image processing. Our hypothesis is that the dataset collected over a semi-diurnal tidal period (>12 hours) could capture the internal representations or feature vector needed for classification. The trained model is then tested on the next 6 hours of CIRs and achieves 98% accuracy, indicating the potential of ML in underwater applications.

Stochastic Channel Modeling for Diffusive Mobile Molecular Communication Systems

In this paper, we develop a mathematical framework for modeling the time-variant stochastic channels of diffusive mobile MC systems. In particular, we consider a diffusive mobile MC system consisting of a pair of transmitter and receiver nano-machines suspended in a fluid medium with a uniform bulk flow, where we assume that either the transmitter, or the receiver, or both are mobile and we model the mobility by Brownian motion. The transmitter and receiver nano-machines exchange information via diffusive signaling molecules. Due to the random movements of the transmitter and receiver nano-machines, the statistics of the channel impulse response (CIR) change over time. We derive closed-form expressions for the mean, the autocorrelation function (ACF), the cumulative distribution function (CDF), and the probability density function (PDF) of the time-variant CIR. Exploiting the ACF, we define the coherence time of the time-variant MC channel as a metric for characterization of the variations of the CIR. The derived CDF is employed for calculation of the outage probability of the system. We also show that under certain conditions, the PDF of the CIR can be accurately approximated by a Log-normal distribution. Based on this approximation, we derive a simple model for outdated channel state information (CSI). Moreover, we derive an analytical expression for evaluation of the expected error probability of a simple detector for the considered MC system. In order to investigate the impact of CIR decorrelation over time, we compare the performances of a detector with perfect CSI knowledge and a detector with outdated CSI knowledge. The accuracy of the proposed analytical expressions is verified via particle-based simulation of the Brownian motion.

Golay Code Based Bit Mismatch Mitigation for Wireless Channel Impulse Response Based Secrecy Generation

The wireless radio communications channel between two legitimate user terminals has long been considered as a renewable source of shared secrecy. As characterization of the channel, the time-varying channel impulse response (CIR) is commonly used for this purpose due to its reciprocity and inherent difficulty in being captured by an adversary. While each terminal generates a secret key at its side, based on its own channel measurements combined with an independent noise component, mismatches may occur in the extracted information or in the obtained bit sequences after a sampling and quantization process. To mitigate such bit mismatches, we propose a method that utilizes the properties of the Golay code to correct or detect the bit mismatches for CIR-based secret key agreement. In our approach, the reconciliation information in the transmission is deliberately designed to hide the original shared information (perfect or imperfect). The correctness of our bit mismatch mitigation algorithm is thoroughly analyzed for all possible scenarios. In addition, the effectiveness of our algorithm is validated through simulations using MATLAB for generated CIR data, the results of which match our theoretical analysis. Our design is shown to be secure and optimal by security analysis and comparisons between our proposed algorithm and its variants. The comparisons with related techniques and the scenarios in which our design can benefit are also discussed.

High-Frequency Acoustic Estimation of Time-Varying Underwater Sparse Channels Using Multiple Sources and Receivers Operated Simultaneously

In this paper, the authors propose a robust underwater acoustic field estimation of the time-varying channel impulse response for simultaneous transmissions using multiple sources and multiple receivers [multi-in multi-out (MIMO)] that closely follows the rapidly time-varying nature of the underwater acoustic channel. The presented algorithm outlines a time-varying underwater channel estimation method based on the channel sparsity characteristic. The method uses the MIMO P-iterative greedy orthogonal matching pursuit algorithm and takes advantage of the sparsity of the underwater acoustic channel. This technique is compared to a trended least square estimation method presented by the authors in a previous publication. Both techniques are demonstrated in a fully controlled environment, using simulated and experimental data, and the results in each case show a significant improvement in the estimation of the channel impulse response.

Parametric Replay-Based Simulation of Underwater Acoustic Communication Channels

This paper presents an underwater acoustic channel simulation methodology that combines parametric modeling with stochastic replay of at-sea measured channel impulse responses. The motivation behind this approach is to extend the scope of use of replay-based methods by allowing some parameterization of the channel properties while complying with some level of realism. Such an approach is beneficial for extensive testing of communication links. The key idea is to deliberately distort the statistics of the experimental channel in order to meet some user-specified constraints. Our approach is based on a relative entropy minimization between the original time-varying channel impulse response and the simulated one. A particular attention is given to constraints on the channel Doppler spread and on the level of covariance between channel taps. The testing capabilities provided by parametric replay-based simulations are illustrated with real data collected in the bay of Brest, France.

Parameter Based Channel Estimation for OFDM Systems Over Time-Varying Channels

This paper investigates the parameter-based (PB) channel estimation for orthogonal frequency division multiplexing over time-varying channels. The time-varying channel impulse response (CIR) can be represented using limited number of channel parameters. Instead of estimating the CIR directly, the PB approach estimates these parameters, from which, the CIR can be regenerated. To estimate these parameters, a differential scheme, utilizing the repetitive structure of the training signal, is proposed in this paper. When expressing the received signal in time---frequency-representation form, the repetitive structure can cause a phase difference between consecutive signal branches. This provides the chance for parameter estimation. Space domain sampling using multiple receive antennas are also employed to gain unique solution for parameter estimation. The effectiveness of the proposed algorithm is demonstrated by computer simulations.

Adaptive B-spline neural network based nonlinear equalization for high-order QAM systems with nonlinear transmit high power amplifier

High bandwidth-efficiency quadrature amplitude modulation (QAM) signaling widely adopted in high-rate communication systems suffers from a drawback of high peak-to-average power ratio, which may cause the nonlinear saturation of the high power amplifier (HPA) at transmitter. Thus, practical high-throughput QAM communication systems exhibit nonlinear and dispersive channel characteristics that must be modeled as a Hammerstein channel. Standard linear equalization becomes inadequate for such Hammerstein communication systems. In this paper, we advocate an adaptive B-Spline neural network based nonlinear equalizer. Specifically, during the training phase, an efficient alternating least squares (LS) scheme is employed to estimate the parameters of the Hammerstein channel, including both the channel impulse response (CIR) coefficients and the parameters of the B-spline neural network that models the HPA's nonlinearity. In addition, another B-spline neural network is used to model the inversion of the nonlinear HPA, and the parameters of this inverting B-spline model can easily be estimated using the standard LS algorithm based on the pseudo training data obtained as a natural byproduct of the Hammerstein channel identification. Nonlinear equalisation of the Hammerstein channel is then accomplished by the linear equalization based on the estimated CIR as well as the inverse B-spline neural network model. Furthermore, during the data communication phase, the decision-directed LS channel estimation is adopted to track the time-varying CIR. Extensive simulation results demonstrate the effectiveness of our proposed B-Spline neural network based nonlinear equalization scheme.

Shallow water acoustic channel modeling with adaptive communications

The underwater acoustic channel is known to be severely bandwidth limited due to sound attenuation by sea water, and interaction with the ocean surface and bottom. Yet, shallow water acoustic channels at high frequencies are little understood, particularly in shallow water environments, and hence the quest for achieving a viable adaptive communication solution has been a challenge that perplexed scientists for a long time. In this abstract, we first take Hodson River estuary as an example to investigate the characterizations of shallow water environments, which mainly comprises the evaluation of key channel parameters such as the scattering function, Doppler shift, coherent bandwidth, coherent time, 2D (i.e., Time-Frequency) time-variant channel impulse response (CIR). The study will also cover channel fading statistics, and water conditions that affect the CIR (e.g., bubble plumes and mddium inhomogeneities). Finally, the models developed will be used to evaluate the achievable performance of channel estimation and adaptive communication systems in shallow water acoustic media.

Modeling and tracking temporal fluctuations for underwater acoustic communications

High-rate, phase-coherent communications through the underwater acoustic channel requires implicitly or explicitly tracking the fluctuations of the channel impulse response. Current techniques estimate the time-varying channel impulse response using observations of signals that have propagated through the channel over some averaging interval and often rely on some method of representing the time variability in the channel. For example, the work by Stojanovic that enabled phase-coherent communications through the underwater channel represented the time-variation as a common phase rotation (Doppler shift) of all taps of the channel impulse response. The tracking of channel fluctuations involves a fundamental trade-off between the number of independent parameters used to represent both the channel impulse response coefficients (i.e., the dimensionality of the estimation problem) and their temporal variations, the rate of channel fluctuations that can be tracked, and the signal to noise ratio (SNR). This talk will present new and review established techniques for modeling time variability and reducing dimensionality in underwater acoustic channel. The performance of these approaches as a function of SNR and rate of channel fluctuation will be compared and their computational complexity will be discussed.

Evaluation of single- and multicarrier methods for underwater communication using data-driven simulations.

Unlike in radio communications, there is no standard statistical model for the channel impulse response in underwater acoustic communications. To compare the performance of different communications strategies requires data collected under common environmental conditions. In the present paper, a data-driven simulator is developed for comparing single- and multicarrier communications algorithms. Models for the time-varying channel impulse response over a particular bandwidth are first constructed using archival experimental data. The models are then driven with novel communications sequences of lesser bandwidth. The output is synthetic received data that can then be demodulated to evaluate communications performance. By applying reciprocity to the single-input, multiple-output archival data, both multiple receive arrays and multiple transmit arrays can be simulated. Communications performance for the synthetic data is compared to actual experimental results. [Work partially supported by the ONR.]

Iterative estimation of doubly selective channels with ICI suppression for OFDM using KL-BEM

Channel estimation for orthogonal frequency division multiplexing (OFDM) systems operating over doubly selective channels is complicated by the existence of intercarrier interference (ICI). This paper extends the idea of estimating time-variant channel impulse response (CIR) via time-averaged CIR proposed by Hijazi, and proposes a low-complexity channel estimation scheme with ICI suppression based on Karhunen-Loeve basis expansion model (KL-BEM), which is in a more general form. In the proposed scheme, the KL-BEM coefficients are obtained through their numerical relationship with the time-averaged CIR. The estimation performance is improved by adopting an expectation-maximization (EM)-based iterative structure combined with a low-complexity ICI equalizer and canceller. Theoretical analysis and simulation results show that the proposed scheme achieves superior performance over the original one with lower computational complexity.