Shallow Water Acoustic Channel Research Articles

Communication Channel Influence on Self Interference Cancellation for In-Band Full-Duplex Underwater Acoustic Systems

This paper employs a self-interference cancellation (IBFD-UWA) system for shallow-water acoustic channels by depending on the model of communication channel for SI cancellation, which is based on geometry to accommodate any possible changes in the underwater environment. UWA channels have propagation characteristics based on the configurations of straight and multi-direction mirrors between a transmitter and a receiver. This propagation at the node itself and between the two nodes to transfer information between the nodes operating in the IBFD mode, orthogonal frequency division multiplex (OFDM) modulation with quadrature phase-shift keying (QPSK) mapping. By using numerical simulations, it can be shown that the developed model is able to accurately describe the underwater acoustic environment. The results indicate that local multipath propagation delays can exceed 1.4s with transmission losses of up to 80dB, and the SIC requires about 4 seconds to reduce SI signals to background noise levels successfully.

Analysis of High-Frequency Communication Channel Characteristics in a Typical Deep-Sea Incomplete Sound Channel

Deep-sea acoustic communication has attracted widespread attention in recent years. Research on deep-sea acoustic communication channel characteristics is essential for the development and system design of deep-sea acoustic communication technologies. However, the structural and spatiotemporal characteristics of deep-sea high-frequency acoustic communication channels (unlike those of shallow-water acoustic communication channels) are poorly understood. Based on a channel measurement experiment in a typical deep-sea incomplete sound channel environment in the South China Sea, this paper analyzes the structural and spatiotemporal characteristics of high-frequency underwater acoustic channels in the direct-arrival and shadow zones. The channel multipath vertical structure, amplitudes of different path clusters, root mean square (RMS) delay spread, and channel temporal coherence are investigated at different depths. The randomness of different path clusters is quantified as the coefficient of variation. The channels in the shadow zone are characterized by a complex multi-path structure, high RMS delay spread, and low temporal coherence. On the contrary, the channels of the direct-arrival zone show multi-path convergence, low RMS delay spread, and high temporal coherence. Understanding the dynamic changes in these parameters can guide the statistical modeling of channels and the design of communication algorithms in different zones in deep-sea high-frequency acoustic communication scenarios.

Deep transfer learning-based variable Doppler underwater acoustic communications

This paper proposes a deep transfer learning (DTL)-based variable Doppler frequency-hopping binary frequency-shift keying underwater acoustic communication system. The system uses a convolutional neural network (CNN) as the demodulation module of the receiver. This approach directly demodulates the received signal without estimating the Doppler. The DTL first uses the simulated communication signal data to complete the CNN training. It then copies a part of the convolution layers from the pre-trained CNN to the target CNN. After randomly initializing the remaining layers for the target CNN, it is trained by the data samples from the specific communication scenarios. During the training process, the CNN learns the corresponding frequency from each symbol in the selected frequency-hopping group through the Mel-spectrograms. Simulation and experimental data processing results show that the performance of the proposed system is better than conventional systems, especially when the transmitter and receiver of the communication system are in variable speed motion in shallow water acoustic channels.

TCN-based M-ary mobile spread spectrum underwater acoustic communication

A deep learning-based direct sequence spread spectrum (DSSS) underwater acoustic (UWA) communication system was proposed in this study to improve the performance of the conventional system under Doppler effects and low signal-to-noise ratio (SNR). The Temporal Convolutional Network (TCN) model was used as the receiver of the system. Simulation results showed that the TCN-based M-ary DSSS UWA communication system outperformed the conventional system under Doppler effects and complex shallow water acoustic channel. A transfertraining algorithm was used for fine-tuning of the model based on sea real-time data to achieve optimal performance. Sea experimental results showed that the fine-tuned TCN model improved the communication effect under Doppler background in the complex dynamic environment of shallow water.

Construction and application of polar codes in OFDM underwater acoustic communication

A polar-coded orthogonal frequency-division multiplexing (OFDM) underwater acoustic communication system was developed, and a Monte Carlo (MC) method for constructing optimal polar codes was proposed to improve the performance of the system in this study. Several optimal polar codes with different code lengths and rates were constructed for a shallow-water acoustic channel. Simulations showed that the polar codes constructed using the MC method rendered superior performance than those constructed using the Bhattacharyya parameter and the Gaussian approximation method. In addition, shallow-water acoustic communication experiments were conducted at Xiamen port. The results show that the developed polar-coded OFDM communication system has excellent performance with 196 out of 200 frames achieving error-free transmission, which is much greater than the performance (157 out of 200) of the LDPC-coded OFDM communication system.

Modeling and channel estimation for piezo-acoustic backscatter assisted underwater acoustic communications

Relying on direct and converse piezoelectric effects, piezo-acoustic backscatter (PAB) technology reflects ambient acoustic signals to enable underwater backscatter communications at near-zero power, which was first realized through a prototype. In this paper, we propose a mathematical model of the PAB assisted underwater acoustic (UWA) communication, and address the sparse channel estimation problem. First, we present a five-stage backscatter process to derive the backscatter coefficient, and propose the channel model for the shallow-water communications. Then, we formulate the shallow-water acoustic channel estimation problem as a sparse vector recovery one according to the compressed sensing theory, and leverage the orthogonal matching pursuit (OMP) algorithm to obtain the channel estimator. Finally, simulation results are provided to corroborate our proposed studies.

Recent results in shallow water acoustic channel estimation and equalization

Shallow water acoustic channel estimation and equalization has been widely studied and explored across the last three decades, with several techniques being proposed that account for the rapidly time-varying nature of the channel. Despite advances in integrating robust channel equalization techniques with channel estimation, incorporating well-known models of acoustic propagation into the equalization setup has been hard. In this work, we will present some of our recent explorations in this regard involving a hybrid setting as well as a model-based simulation to test the performance of simple equalization schemes under a variety of channel conditions. The shallow water acoustic channel is first estimated from experimental field data, and the real-time channel estimates are used to simulate an end-to-end communication system with ambient noise at different signal-to-noise ratios (SNR). In tandem, we also simulate a time-varying shallow water acoustic channel under similar oceanic conditions using the Bellhop model, along with a communication system with similar signaling schemes and SNR variations. Equalization results from both the hybrid and model-based settings will be presented and implications of including machine learning to learn the channel parameters from both setups to inform the channel equalizers will be explored.

Prefiltered Single-Carrier Frequency-Domain Equalization for Binary CPM over Shallow Water Acoustic Channel

The continuous phase modulation (CPM) technique is an excellent solution for underwater acoustic (UWA) channels with limited bandwidth and high propagation attenuation. However, the severe intersymbol interference is a big problem for the algorithm applying in shallow water. To solve this problem, an algorithm for prefiltered single-carrier frequency-domain equalization (PF-SCFDE) is presented in this paper. The regular whitening filter is replaced by a prefilter in the proposed algorithm. The output information sequence of this prefilter contains the forward information. To improve the performance, the output of the equalizer, combined with the forward information, is used to make the maximum likelihood estimation. The simulation results with minimum-shift keying and Gaussian-filtered minimum-shift keying signals over shallow water acoustic channels with low root mean square delay spread demonstrate that PF-SCFDE outperformed the traditional single-carrier frequency-domain equalization (SCFDE) by approximately 1 dB under a bit error rate (BER) of 10−4. A shallow sea trial has demonstrated the effectiveness of PF-SCFDE; PF-SCFDE had a reduction in BER of 18.35% as compared to the traditional SCFDE.

Estimation and interpretation of multipath channel activity using braiding techniques

Robust underwater acoustic channel estimation is critical towards improving communications efforts and enhancing awareness of changing environments. To explore these channels in-depth, machine learning algorithms are developed through feature geometric representations, referred to as “braiding,” to interpret multipath ray bundles within shallow water acoustic channels in two ways. The first application of this work predicts the number of reflections an acoustic signal may undergo through the environment by applying known physical parameters and braid features. The second application explores the importance of a braid feature within the acoustic channel for estimation purposes by using braid path information. Three unique machine learning techniques are trained to predict the applications using a diverse set of shallow water acoustic channels generated through the BELLHOP model. Machine learning models developed for the applications demonstrate high testing accuracies with an accuracy of 86.70% in the first application and an accuracy of 99.94% in the second application. As a further demonstration, braid feature representations and model predictions are used for channel estimation and determining the number of reflections using SPACE08 field data.

Deep learning-based M-ary spread spectrum communication system in shallow water acoustic channel

A deep learning-based M-ary spread spectrum (SS) underwater acoustic (UWA) communication system is proposed. The long short-term memory (LSTM) model is used as the receiver of the system, enabling the direct demodulation of received signals without de-carrier and de-spreading operations. LSTM architecture-based neural network models are fed with the time-domain waveform of each symbol during the training process to learn the source information carried by each symbol. Simulation results show that the deep learning-based M-ary SS UWA communication system outperforms the conventional system under the low signal-to-noise ratio and complex shallow water acoustic channel. For optimal performance, different LSTM models can be selected according to the simulation conditions. Furthermore, a water tank experiment simulating the complex shallow water channel was conducted and the results show that experimental equipment, different training sample acquisition methods, and LSTM models can affect the performance of the system.

Analysis of Doppler and Multipath on Orthogonal Chirp Division Multiplexing in Shallow Water Acoustic Channel

In the scenario of underwater acoustic communication, Orthogonal Frequency Division Multiplexing (OFDM) is widely used for its high spectral efficiency and high transmission rate. However, OFDM is sensitive to Doppler shift, which will generate inter-carrier interference to degrade bit error rate performance. Orthogonal Chirp Division Multiplexing (OCDM) consists of multiplexing a number of chirp waveforms that are mutually orthogonal with each other and share the same bandwidth and time slot, achieving good robustness against multipath and Doppler shift. This study reviews the concepts of Orthogonal Chirp Division Multiplexing and combines this multicarrier communication scheme with an underwater acoustic channel. Unlike previous studies analyzing within the signal domain, this study formulates the mathematical model of OCDM in shallow water acoustic channel and illustrates the reason why OCDM can outperform OFDM in both Doppler robustness and multipath robustness, which is a necessary and solid complement for OCDM in the further development of underwater acoustic communication.

Autonomous learning and interpretation of channel multipath scattering using braid manifolds in underwater acoustic communications

In this work, we explore machine learning through a model-agnostic feature representation known as braiding, that employs braid manifolds to interpret multipath ray bundles. We generate training and testing data using the well-known BELLHOP model to simulate shallow water acoustic channels across a wide range of multipath scattering activity. We examine three different machine learning techniques-k-nearest neighbors, random forest tree ensemble, and a fully connected neural network-as well as two machine learning applications. The first application applies known physical parameters and braid information to determine the number of reflections the acoustic signal may undergo through the environment. The second application applies braid path information to determine if a braid is an important representation of the channel (i.e., evolving across bands of higher amplitude activity in the channel). Testing accuracy of the best trained machine learning algorithm in the first application was 86.70% and the testing accuracy of the second application was 99.94%. This work can be potentially beneficial in examining how the reflectors in the environment changeover time while also determining relevant braids for faster channel estimation.

Time reversal pulse position modulation communication in shallow water acoustic channels

The remote sensing and control of biomimetic robot fishes is an important but challenging task because the available underwater acoustic communication cannot accommodate their size, power consumption, and price. Hence, we propose a power-efficient, low-complexity time reversal pulse position modulation communication scheme, which partitions the available bandwidth of a transducer into several different sub-bands using different linear frequency modulated (LFM) pulses. The information is reflected by the position of the LFM pulses with different start and end frequencies for adjacent symbols. In the receiver, the Doppler factor is obtained by cross-correlating two hyperbolic frequency modulation signals transmitted before and after the modulated signal, and the channel response is estimated by match filtering the sync preamble. Furthermore, time reversal and bandpass filtering are performed to mitigate intrasymbol and intersymbol interferences caused by the multipath effects of underwater acoustic channels, respectively. The effectiveness and robustness of the proposed communication scheme are confirmed experimentally.

Sonar image reconstruction method based on adaptive compensation of very shallow water acoustic channel features

Aiming at the problem of eliminating the influence of channel interference on the detection results in the process of using sonar to detect very shallow water targets, a sonar image reconstruction method based on adaptive compensation of very shallow water acoustic channel features is proposed. Standard acoustic source and remote server are used to complete the adaptive calibration process of channel characteristic parameters before detection. The calibrated channel characteristic parameters are used in the subsequent beamforming algorithm to compensate the channel interference. The time-of-flight parameters calculated by the compensation algorithm are used in the subsequent point cloud generation and image reconstruction. Simulation and field test show that the proposed compensation method can improve sonar target detection ability by 25% in very shallow water and complex acoustic channel environment, and the quality of sonar image reconstruction is significantly improved.

Braid representation of the shallow water acoustic channel to interpret temporal evolution of multipath arrivals

We present a morphological representation of the underwater acoustic channel that employs geometric braids to gain real-time knowledge of the dominant multipath activity. The key idea is to incorporate braid manifolds in tandem with non-convex mixed norm optimization techniques to track the temporal evolution of multipath arrivals. Specifically, we focus on channel braids that manifest as rapidly fluctuating high-energy taps in the delay spread as well as the delay-Doppler scattering function. Multiple braids can also be topologically combined using braid operations to interpret oceanic phenomena such as caustics and surface wave focusing, among others. We present techniques for adaptively updating the channel braids and their overlap patterns to reflect the temporal evolution of the shallow water acoustic channel. We evaluate the performance of proposed morphological channel estimation technique in terms of the normalized prediction error and computational time. Results based on numerical channel simulations based on diverse oceanic and experimental conditions as well as experimental field data from the SPACE08 experiment will be presented.

Support-Constrained Mixed-Norm Optimization Techniques for Estimating Multipath Activity in Shallow Water Acoustic Channels

In this article, we propose channel estimation techniques for shallow water acoustic channels that exhibit rapidly fluctuating high-amplitude multipath activity across different regions of the channel support. Specifically, we impose support constraints on the channel impulse response that confine tracking the shallow water acoustic channel to within the most active regions within the channel delay spread. The key idea is to focus on channel estimation performance through the banded nature of the multipath arrivals in the delay versus time channel that we denote as subregions of the channel delay spread. Because we are examining sparse shallow water acoustic channels, we adopt the well-known Lasso metric and related mixed-norm optimization techniques, but in contrast to traditional sparse sensing approaches, we use masked constraints to prioritize the estimation of regions of interest within the channel delay spread. This provides a tradeoff between computational time and estimation performance that we explore in detail over experimental field data from the SPACE08 experiment and over simulated channels emulating diverse oceanic conditions.

Shallow water channel estimation with energy efficient transmitted signal design.

This paper proposes channel estimation using energy efficient transmission of signal dictionaries for shallow water acoustic communications. Specifically, the multi-columned structure of the channel delay spread is exploited to design partially sampled dictionary in a two-dimensional (2-D) frequency representation of the channel. The key contribution of this work is to achieve considerable energy saving in the transmission of complex exponential signals, designed specifically for real-time shallow water channel estimation at the receiver. This is accomplished by harnessing 2-D frequency localization with compressive transmission and modified-compressive sensing with prior information to exploit the sparse structure of the rapidly fluctuating shallow water acoustic channel in real time. The proposed technique reduces demands on transmitted signal energy by harnessing the reconstruction ability of sparse sensing while retaining key non-sparse channel elements that represent important multipath phenomena. Numerical evidence based on experimental channel estimates demonstrates the efficacy of the proposed work.

Employing braid theory to disentangle overlapping oceanic events in the shallow water acoustic channel

The shallow water acoustic channel is well-known to exhibit rapid temporal fluctuations due to multipath effects, fluid motion as well as focusing events in the ocean. These events often overlap temporally thus contributing in unpredictable ways to the channel delay spread. We will present recent results from employing geometric signal processing techniques to shallow water channel estimation and will introduce new ways to identify and disambiguate overlapping oceanic phenomena within the channel delay spread. Specifically, we will explore how geometric braid theory and related operations can be employed to disentangle overlapping channel braids, and explore the efficacy of such signal processing techniques across diverse channel representations. Finally, we will discuss the importance of capturing oceanic phenomena at the appropriate resolution of time, frequency, and sparsity and the trade-offs in accuracy involved based on the chosen channel representation(s).

Harnessing geometric techniques for robust real-time estimation of shallow water acoustic channels

The shallow water acoustic channel is well-known to exhibit rapid fluctuations in delay spread, which poses a fundamental roadblock to real-time channel estimation, particularly under moderate to rough sea conditions. This talk will summarize recent research on multiple representations of the shallow water acoustic channel and present the relative merits of each channel representation in localizing different channel effects. In particular, we will examine the efficacy of employing geometric signal processing techniques to adaptively track the shallow water acoustic channel in each representation. Results based on experimental field data as well as channel simulations under varying environmental conditions will be presented.The shallow water acoustic channel is well-known to exhibit rapid fluctuations in delay spread, which poses a fundamental roadblock to real-time channel estimation, particularly under moderate to rough sea conditions. This talk will summarize recent research on multiple representations of the shallow water acoustic channel and present the relative merits of each channel representation in localizing different channel effects. In particular, we will examine the efficacy of employing geometric signal processing techniques to adaptively track the shallow water acoustic channel in each representation. Results based on experimental field data as well as channel simulations under varying environmental conditions will be presented.

Modeling of the power delay profile for underwater acoustic channels—Effects of out-of-plane scattering and reverberation

Long reverberation tails seem to be a common feature of shallow water acoustic channel impulse responses. We show examples from two significantly different environments along the Norwegian coast, and demonstrate that 2-D propagation modeling fails to reproduce the long tails observed. The continuous nature of the impulse responses and the low correlation and slow decay rates of the tails point towards reverberation as an important mechanism behind the observations. The effects of reverberation and out-of-plane scattering are then included in the modeling by employing the 3-D ray-based model Rev3D. Scattering from the sea surface and sea bottom are described by scattering-strength functions including azimuthal variation. The modeling results indicate that the long reverberation tails can be explained by 3-D out-of-plane scattering. Although the measured levels are not perfectly matched, the importance of including reverberation in the modeling is clearly demonstrated.