Search for: All records

Traditional methods for coding underwater acoustic communications are bound to be surpassed by methods optimizing for source-channel coding jointly. However, the complexity of joint-optimization has thwarted successful breakthroughs in this area. We, therefore, present a novel approach, where we model the coding problem as the translation problem of the input sequence to another ‘language’, depending on the estimated channel conditions. We use Long Short-Term Memory (LSTM)-based sequence-to-sequence models to enable this and explain our approach in detail. 

Scalable Video Coding (SVC) has been widely used in video transmissions. However, inappropriate SVC structures may lead to received video quality lower than user’s requirement or resource waste, especially in underwater time-varying channels. In this work, an adaptive cross-layering solution is proposed and validated for video transmissions in underwater acoustic multicast networks, namely Adaptive Scalable Video Transmission (ASVTuw). In ASVTuw, the transmitter collects over time the information about the channel states and the users’ video quality requirements to adaptively select the SVC video structures and transmission schemes, using Machine Learning (ML). At-sea experiments were conducted to collect the required acoustic data. The collected data were then used in MATLAB simulations to validate the ASVTuw. The results show that the usage of ASVTuw avoids resource wasting from transmitting redundant SVC substreams and satisfies the multicast users’ video quality requirements effectively with higher flexibility compared with the existing noncross-layering designs. 

With the rapid growth of Machine Learning (ML) in recent years, more and more technical issues, which were usually solved by model-based solutions, have an opportunity to be solved with data driven solutions. Underwater Doppler effect was tackled with model-based solutions in tracking the motion and compensating the interference caused by multipath Doppler effect in communications. However, a too complex model for the harsh underwater conditions leads to massive computation and becomes an obstacle for the real-time Doppler compensation. In this research, we adopt ML techniques to solve underwater Doppler issues. We propose ML-based tracking and a tracking-aid ML-based compensation. The results show that joint tracking and compensation method have tap choosing accuracy 96.7%, 86.7%, 100% and 93.3% in different power ratios of the two-dominant path condition with fine tree, linear Support Vector Machine (SVM), quadratic SVM and cubic SVM. 

Orthogonal Signal Division Multiplexing (OSDM) has shown great robustness against multipath and Doppler effects in underwater acoustic channels, while however having a low spectral efficiency. In this work, a novel transmission method, named Spatial Modulation-based OSDM (SM-OSDM), is proposed to improve the spectral efficiency while keeping the Bit Error Rate (BER) low, where multiple transducers are utilized at the transmitter side but only one is active in each time slot. The simulation results prove that the SMOSDM offers higher spectral efficiency than Single-Input SingleOutput (SISO)-OSDM and lower BER compared with MultipleInput Multiple-Output (MIMO)-OSDM. 

Pulse Position Modulation (PPM) has shown great advantages by being non-coherently implementable. However, PPM suffers from the high multipath delay in underwater acoustic channels. To be robust against the effects of multipath delay and additive noise in underwater acoustic communications, this project proposes a novel modulation scheme that is nonlinear and that can be implemented non-coherently, called the Circular Time Shift Modulation (CTSM) based on the high autocorrelation property of Zero-Correlation-Zone (ZCZ) signals. The performance of CTSM in terms of bit error rate is investigated by emulations based on real underwater acoustic channels collected in the Gulf of La Spezia, Italy, using the Littoral Ocean Observatory Network (LOON) testbed hosted at the NATO Centre for Maritime Research and Experimentation (CMRE) in June 2021. The results have shown that the CTSM system has higher robustness compared with PPM in underwater acoustic communications. 

Achieving reliable acoustic wireless video transmissions in the extreme and uncertain underwater environment is a challenge due to the limited bandwidth and the error-prone nature of the channel. Aiming at optimizing the received video quality and the user's experience, an adaptive solution for underwater video transmissions is proposed that is specifically designed for Multi-Input Multi-Output (MIMO -based Software-Defined Acoustic Modems (SDAMs . To keep the video distortion under an acceptable threshold and to keep the Physical-Layer Throughput (PLT high, cross-layer techniques utilizing diversity-spatial multiplexing and Unequal Error Protection (UEP are presented along with the scalable video compression at the application layer. Specifically, the scalability of the utilized SDAM with high processing capabilities is exploited in the proposed structure along with the temporal, spatial, and quality scalability of the Scalable Video Coding (SVC H.264/MPEG-4 AVC compression standard. The transmitter broadcasts one video stream and realizes multicasting at different users. Experimental results at the Sonny Werblin Recreation Center, Rutgers University-NJ, are presented. Several scenarios for unknown channels at the transmitter are experimentally considered when the hydrophones are placed in different locations in the pool to achieve the required SVC-based video Quality of Service (QoS and Quality of Experience (QoE given the channel state information and the robustness of different SVC scalability. The video quality level is determined by the best communication link while the transmission scheme is decided based on the worst communication link, which guarantees that each user is able to receive the video with appropriate quality.