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A star modulation network for wireless image semantic transmission.

Xiangcheng Li1,2, Dongri Ban1, Zhaokai Ruan1

  • 1The School of Computer, Electronics and Information, Guangxi University, Nannning, 53004, China.

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Summary
This summary is machine-generated.

This study introduces STARJSCC, a lightweight framework for efficient wireless image semantic transmission. It improves performance and adaptability across various conditions while reducing computational complexity and model size.

Keywords:
Channel bandwidth ratio adaptationChannel state adaptationJoint source-channel codingLightweight

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Area of Science:

  • Wireless communication
  • Deep learning
  • Image processing

Background:

  • Deep joint source-channel coding (DEEPJSCC) is widely researched for semantic communication.
  • Existing DEEPJSCC methods face challenges with efficiency, model size, and computational complexity.

Purpose of the Study:

  • To develop a lightweight DEEPJSCC framework for efficient wireless image semantic transmission.
  • To enhance adaptability to varying channel conditions and transmission rates.

Main Methods:

  • Introduced STARJSCC, a novel lightweight DEEPJSCC framework.
  • Incorporated a channel state adaptive module (CSA Mod) for dynamic adaptation.
  • Utilized a decoupled static semantic compression (SC) mask for rate control.

Main Results:

  • STARJSCC demonstrated superior performance and adaptability compared to baseline schemes.
  • Achieved up to 2.73 dB improvement on high-resolution image transmission.
  • Significantly reduced model parameters, computational complexity, and storage overhead.

Conclusions:

  • STARJSCC offers a viable solution for high-quality wireless image transmission in resource-constrained environments.
  • The framework provides flexibility and efficiency for semantic communication.