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CNN and Attention-Based Joint Source Channel Coding for Semantic Communications in WSNs.

Xinyue Liu1, Zhen Huang1, Yulu Zhang1

  • 1School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 401331, China.

Sensors (Basel, Switzerland)
|February 10, 2024
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Summary

This study introduces an Attention-based Adaptive Coding (AAC) module for wireless sensor networks (WSNs) to improve communication robustness and reduce bandwidth pressure. The semantic communication approach effectively adapts to varying signal conditions, enhancing data transmission efficiency.

Keywords:
attention mechanismdeep neural networkjoint source channel codingmobile edge computingsemantic communicationswireless sensor networks

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Area of Science:

  • Wireless Sensor Networks
  • Communication Engineering
  • Artificial Intelligence

Background:

  • Wireless Sensor Networks (WSNs) are crucial for real-time applications but face challenges from data proliferation and dynamic environments due to 5G and mobile edge computing (MEC).
  • Ensuring robust communication and managing bandwidth pressure are key issues in large-scale WSN deployments.

Purpose of the Study:

  • To propose a semantic communication solution addressing WSN communication challenges.
  • To develop a flexible Attention-based Adaptive Coding (AAC) module suitable for resource-constrained WSN devices.

Main Methods:

  • Proposed a flexible Attention-based Adaptive Coding (AAC) module integrating window and channel attention mechanisms.
  • Developed an end-to-end Joint Source Channel Coding (JSCC) scheme for image semantic communication utilizing the AAC module.
  • Trained a single model adaptable across various Signal-to-Noise Ratio (SNR) environments.

Main Results:

  • The proposed JSCC scheme with the AAC module outperformed existing deep JSCC schemes on diverse image datasets.
  • Experimental results validated the AAC module's efficacy in dynamically adjusting semantic information based on channel state.
  • The model demonstrated successful training and improved performance across a range of SNRs.

Conclusions:

  • The proposed semantic communication approach, featuring the AAC module, effectively enhances WSN communication robustness and bandwidth efficiency.
  • The AAC module's adaptive capabilities allow for a single model to perform optimally in varied communication environments.
  • This research offers a promising solution for reliable and efficient data transmission in advanced WSN applications.