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Deep Learning-Based Spread-Spectrum FGSM for Underwater Communication.

Zeyad A H Qasem1, Hamada Esmaiel1,2, Haixin Sun1

  • 1Department of Information and Communication, School of Informatics, Xiamen University, Xiamen 361005, China.

Sensors (Basel, Switzerland)
|October 31, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a novel spread-spectrum fully generalized spatial modulation (SS-FGSM) scheme for underwater acoustic communication. It enhances data rates and energy efficiency by encoding information in antenna and spreading code indices, reducing power consumption.

Keywords:
FGSMMIMOSMdeep learningenergy efficiency and spectral efficiencyspread spectrumunderwater acoustic communications

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Area of Science:

  • Underwater acoustic communication
  • Wireless communication systems
  • Signal processing

Background:

  • Challenges in underwater acoustic communication include limited bandwidth and energy constraints for sensor nodes.
  • Traditional multi-input multi-output (MIMO) is hindered by inter-channel interference and multipath effects in underwater environments.
  • Spatial Modulation Techniques (SMTs) offer a potential solution by utilizing antenna spatial indices for data transmission.

Purpose of the Study:

  • To propose a novel spatial modulation technique, Spread-Spectrum Fully Generalized Spatial Modulation (SS-FGSM), for underwater acoustic communication.
  • To enhance data transmission efficiency and reduce energy consumption in underwater sensor networks.
  • To develop a low-complexity deep learning (DL) detector for the proposed SS-FGSM scheme.

Main Methods:

  • The proposed SS-FGSM scheme encodes information bits using constellation data symbols, transmitter antenna indices, and predefined spreading code indices.
  • Information bits are primarily transmitted via antenna and spreading code indices, minimizing physical data symbol transmission.
  • A low-complexity deep learning (DL) detector is designed to mitigate receiver computational complexity for the SS-FGSM scheme.

Main Results:

  • The SS-FGSM scheme significantly improves system data rate and energy efficiency compared to conventional SMTs.
  • The proposed DL detector enhances performance by reducing the average bit error rate and receiver computational complexity.
  • Simulation results validate the effectiveness of the DL-based SS-FGSM (DLSS-FGSM) for underwater communication.

Conclusions:

  • The SS-FGSM scheme effectively addresses bandwidth limitations and energy constraints in underwater acoustic communication.
  • The integration of a DL detector provides a computationally efficient and high-performance solution for underwater SMTs.
  • This research advances underwater communication by offering a more efficient and sustainable approach for sensor networks.