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This paper presents GnartDeepSC, a lightweight semantic communication system for IoT image analysis. We introduce the Lightweight Semantic Gated CNN (LSGCNN) block that combines multi-branch architecture with adaptive gating to efficiently extract task-relevant features. Unlike computationally intensive ViT-based methods, GnartDeepSC achieves 90.2% parameter reduction while maintaining 48.3% classification accuracy at –6 dB SNR (13.1% improvement over ViTDeepSC). The system supports simultaneous image reconstruction and classification with \(3.0\times \) faster inference (2.84 ms average latency) and 34.8% energy savings (96.22 mJ per image). Experimental results demonstrate superior robustness across all SNR levels, achieving 42.1 dB PSNR at 18 dB SNR. These characteristics make GnartDeepSC ideal for resource-constrained IoT deployments requiring real-time semantic communication.

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Lightweight CNN-Based Semantic Communication for Image Analysis

  • 会议论文

  • 发表日期：

    2026年

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  • Tung Son Do,
  • Quang Tuan Do,
  • Manh Cuong Ho,
  • Minh Phuoc Nguyen,
  • Md. Mahrab Islam,
  • Sungrae Cho

摘要

This paper presents GnartDeepSC, a lightweight semantic communication system for IoT image analysis. We introduce the Lightweight Semantic Gated CNN (LSGCNN) block that combines multi-branch architecture with adaptive gating to efficiently extract task-relevant features. Unlike computationally intensive ViT-based methods, GnartDeepSC achieves 90.2% parameter reduction while maintaining 48.3% classification accuracy at –6 dB SNR (13.1% improvement over ViTDeepSC). The system supports simultaneous image reconstruction and classification with \(3.0\times \) faster inference (2.84 ms average latency) and 34.8% energy savings (96.22 mJ per image). Experimental results demonstrate superior robustness across all SNR levels, achieving 42.1 dB PSNR at 18 dB SNR. These characteristics make GnartDeepSC ideal for resource-constrained IoT deployments requiring real-time semantic communication.