Spread Spectrum Modulation with Grassmannian Constellations for Mobile Multiple Access Underwater Acoustic Channels

The objective of this study is to evaluate Grassmannian constellations combined with a spread spectrum multiple access scheme for underwater acoustic mobile multiple access communication systems. These communication systems enable the coordination of a fleet of Autonomous Underwater Vehicles (AUVs) from a surface or bottom control unit, e.g., a boat. Due to its robustness against phase rotation, the demodulator of Grassmannian constellations uses non-coherent detection, and the main advantage of such modulation lies in the spectrum efficiency gain with respect to conventional differential modulation. The communication system under study in this paper consists of (i), at the transmitter side, a Grassmannian modulation used in an orthogonal spread spectrum multiple access scheme called Multiuser Hyperbolic Frequency Modulation (MU-HFM) and (ii), at the receiver side, a non-coherent array decoder. The modulation and demodulation are presented as well as the considered spreading sequences. Finally, performances of the proposed transmission scheme are evaluated over replayed underwater acoustic channel responses collected at sea by a multi-sensor acoustic acquisition system.Spread Spectrum Modulation with Grassmannian Constellations for Mobile Multiple Access Underwater Acoustic ChannelspublishedVersio

Cellular Underwater Wireless Optical CDMA Network: Potentials and Challenges

Underwater wireless optical communications is an emerging solution to the expanding demand for broadband links in oceans and seas. In this paper, a cellular underwater wireless optical code division multiple-access (UW-OCDMA) network is proposed to provide broadband links for commercial and military applications. The optical orthogonal codes (OOC) are employed as signature codes of underwater mobile users. Fundamental key aspects of the network such as its backhaul architecture, its potential applications and its design challenges are presented. In particular, the proposed network is used as infrastructure of centralized, decentralized and relay-assisted underwater sensor networks for high-speed real-time monitoring. Furthermore, a promising underwater localization and positioning scheme based on this cellular network is presented. Finally, probable design challenges such as cell edge coverage, blockage avoidance, power control and increasing the network capacity are addressed.Comment: 11 pages, 10 figure

Automated tracking of the Florida manatee (Trichechus manatus)

The electronic, physical, biological and environmental factors involved in the automated remote tracking of the Florida manatee (Trichechus manatus) are identified. The current status of the manatee as an endangered species is provided. Brief descriptions of existing tracking and position locating systems are presented to identify the state of the art in these fields. An analysis of energy media is conducted to identify those with the highest probability of success for this application. Logistic questions such as the means of attachment and position of any equipment to be placed on the manatee are also investigated. Power sources and manateeborne electronics encapsulation techniques are studied and the results of a compter generated DF network analysis are summarized

Adaptive Modulation Schemes for Underwater Acoustic OFDM Communication

High data rate communication is challenging in underwater acoustic (UA) communication as UA channels vary fast along with the environmental factors. A real-time Orthogonal frequency-division multiplexing (OFDM) based adaptive UA communication system is studied in this research employing the National Instruments (NI) LabVIEW software and NI CompactDAQ device. The developed adaptive modulation schemes enhance the reliability of communication, guarantee continuous connectivity, ensure maximum performance under a fixed BER at all times and boost data rate

Adapting Deep Learning for Underwater Acoustic Communication Channel Modeling

The recent emerging applications of novel underwater systems lead to increasing demand for underwater acoustic (UWA) communication and networking techniques. However, due to the challenging UWA channel characteristics, conventional wireless techniques are rarely applicable to UWA communication and networking. The cognitive and software-defined communication and networking are considered promising architecture of a novel UWA system design. As an essential component of a cognitive communication system, the modeling and prediction of the UWA channel impulse response (CIR) with deep generative models are studied in this work. Firstly, an underwater acoustic communication and networking testbed is developed for conducting various simulations and field experiments. The proposed test-bed also demonstrated the capabilities of developing and testing SDN protocols for a UWA network in both simulation and field experiments. Secondly, due to the lack of appropriate UWA CIR data sets for deep learning, a series of field UWA channel experiments have been conducted across a shallow freshwater river. Abundant UWA CIR data under various weather conditions have been collected and studied. The environmental factors that significantly affect the UWA channel state, including the solar radiation rate, the air temperature, the ice cover, the precipitation rate, etc., are analyzed in the case studies. The obtained UWA CIR data set with significant correlations to weather conditions can benefit future deep-learning research on UWA channels. Thirdly, a Wasserstein conditional generative adversarial network (WCGAN) is proposed to model the observed UWA CIR distribution. A power-weighted Jensen–Shannon divergence (JSD) is proposed to measure the similarity between the generated distribution and the experimental observations. The CIR samples generated by the WCGAN model show a lower power-weighted JSD than conventional estimated stochastic distributions. Finally, a modified conditional generative adversarial network (CGAN) model is proposed for predicting the UWA CIR distribution in the 15-minute range near future. This prediction model takes a sequence of historical and forecast weather information with a recent CIR observation as the conditional input. The generated CIR sample predictions also show a lower power-weighted JSD than conventional estimated stochastic distributions

Spread-spectrum techniques for environmentally-friendly underwater acoustic communications

PhD ThesisAnthropogenic underwater noise has been shown to have a negative impact on marine life. Acoustic data transmissions have also been shown to cause behavioural responses in marine mammals. A promising approach to address these issues is through reducing the power of acoustic data transmissions. Firstly, limiting the maximum acoustic transmit power to a safe limit that causes no injury, and secondly, reducing the radius of the discomfort zone whilst maximising the receivable range. The discomfort zone is dependent on the signal design as well as the signal power. To achieve these aims requires a signal and receiver design capable of synchronisation and data reception at low-received-SNR, down to around −15 dB, with Doppler effects. These requirements lead to very high-ratio spread-spectrum signaling with efficient modulation to maximise data rate, which necessitates effective Doppler correction in the receiver structure. This thesis examines the state-of-the-art in this area and investigates the design, development and implementation of a suitable signal and receiver structure, with experimental validation in a variety of real-world channels. Data signals are designed around m-ary orthogonal signaling based on bandlimited carrierless PN sequences to create an M-ary Orthogonal Code Keying (M-OCK) modulation scheme. Synchronisation signal structures combining the energy of multiple unique PN symbols are shown to outperform single PN sequences of the same bandwidth and duration in channels with low SNR and significant Doppler effects. Signals and receiver structures are shown to be capable of reliable communications with band of 8 kHz to 16 kHz and transmit power limited to less than 170.8 dB re 1 μPa @ 1m, or 1W of acoustic power, over ranges of 10 km in sea trials, with low-received-SNR below −10 dB, at data rates of up to 140.69 bit/s. Channel recordings with AWGN demonstrated limits of signal and receiver performance of BER 10−3 at −14 dB for 35.63 bit/s, and −8.5 dB for 106.92 bit/s. Piloted study of multipath exploitation showed this performance could be improved to −10.5 dB for 106.92 bit/s by combining the energy of two arrival paths. Doppler compensation techniques are explored with experimental validation showing synchronisation and data demodulation at velocities over ranges of ±2.7m/s. Non-binary low density parity check (LDPC) error correction coding with M-OCK signals is investigated showing improved performance over Reed-Solomon (RS) coding of equivalent code rate in simulations and experiments in real underwater channels. The receiver structures are implemented on an Android mobile device with experiments showing live real-time synchronisation and data demodulation of signals transmitted through an underwater channel.UK Engineering and Physical Sciences Research Council (EPSRC): PhD Doctoral Training Account (DTA)

Underwater Localization in a Confined Space Using Acoustic Positioning and Machine Learning

Localization is a critical step in any navigation system. Through localization, the vehicle can estimate its position in the surrounding environment and plan how to reach its goal without any collision. This thesis focuses on underwater source localization, using sound signals for position estimation. We propose a novel underwater localization method based on machine learning techniques in which source position is directly estimated from collected acoustic data. The position of the sound source is estimated by training Random Forest (RF), Support Vector Machine (SVM), Feedforward Neural Network (FNN), and Convolutional Neural Network (CNN). To train these data-driven methods, data are collected inside a confined test tank with dimensions of 6m x 4.5m x 1.7m. The transmission unit, which includes Xilinx LX45 FPGA and transducer, generates acoustic signal. The receiver unit collects and prepares propagated sound signals and transmit them to a computer. It consists of 4 hydrophones, Red Pitay analog front-end board, and NI 9234 data acquisition board. We used MATLAB 2018 to extract pitch, Mel-Frequency Cepstrum Coefficients (MFCC), and spectrogram from the sound signals. These features are used by MATLAB Toolboxes to train RF, SVM, FNN, and CNN. Experimental results show that CNN archives 4% of Mean Absolute Percentage Error (MAPE) in the test tank. The finding of this research can pave the way for Autonomous Underwater Vehicle (AUV) and Remotely Operated Vehicle (ROV) navigation in underwater open spaces