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    https://www.researchgate.net/profile/Bevek-Subba
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Bevek Subba

Project - A Study on Next Generation Underwater Acoustic Communication Systems with Orthogonal Time Frequency Space

Underwater Acoustic Communication (UAC) has garnered lot of attention due to its crucial role in enabling critical applications in marine science, resource exploration, defence, environmental monitoring, and disaster management. With the vast majority of Earth’s surface covered by water, there is a growing demand for reliable and effective communication technique in underwater environment. Thus, global UAC market has been experiencing a significant growth. Increasing demand for reliable communication in subsea environment has driven the proliferation of advanced UAC systems, and innovations in UAC technologies have been paramount to this development. However, UWA channel is one of the most challenging channels due to its time-varying and doubly dispersive characteristics (multipath delay spread and Doppler spread). These challenges significantly hinder the reliability and performance of UAC systems. The importance of reliable UAC for variety of underwater applications necessitates the investigation of next-generation UAC systems that are envisioned to provide reliable and robust communication under high Doppler and high mobility scenario.

Orthogonal Time Frequency Space (OTFS) modulation has emerged as a promising solution for next-generation UAC systems to address these challenges by supporting tolerance to fast time-varying channel characteristics and better resilience to Doppler shifts and multipath interference compared to Orthogonal Frequency Division Multiplexing (OFDM). By representing OTFS in delay-Doppler (DD) domain, it can exploit sparsity and capture the dynamic characteristics of the channel. This makes OTFS suitable for the complex UWA environment. Despite its potential, OTFS in UAC systems faces significant technical hurdles. Channel estimation in the DD domain is more challenging compared to the Time-Frequency (TF) domain since it requires accurately identifying both delay and Doppler characteristics for each multipath component. UAC exhibits non-stationary noise and non-linear interferences, which poses challenges to channel estimation and equalization. Further, the high Doppler effect has the tendency to make the channel response deviate from sparsity.

The objective of this research is to enhance the reliability of OTFS-based UAC systems by improving channel estimation and equalization. Model-driven Machine Learning (ML) is proposed to provide framework to combine the compressive sensing model with the data-driven ML. The method incorporates compressive sensing with the Deep Neural Network (DNN) for learning the channel characteristics to obtain better channel estimation. Integration of ML techniques help to mitigate interference, model non-linearities and adapt dynamically to channel variations. Joint estimation and equalization can share learned parameters. This research will establish OTFS as a modulation scheme for next-generation UAC systems. Improvement in the reliability and system robustness are the expected outcomes, paving the way for advanced marine technologies by considering real-world underwater scenarios.

Research Areas and Interests

  • Underwater Communication
  • Wireless and Mobile Communication
  • Antenna Design
  • Optical Communication
  • Cognitive Radio

Qualifications

  • M. Tech. in Communication Systems, SRM University (Chennai), 2017.

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