通过信噪比嵌入实现轻量级自适应图像传输，性能超前沿方案。
SNR-EQ-JSCC: Joint Source-Channel Coding with SNR-Based Embedding and Query
- 将信噪比嵌入注意力块，动态调整关注权重以适配信道变化。
- 在图像传输中实现更高清晰度与感知质量，计算开销仅为6.38%。
- 仅需平均信噪比即可工作，适合信道反馈不稳定的场景。
针对联合源信道编码（JSCC）语义通信系统中动态信道的影响问题，本文提出一种轻量级信道自适应语义编码架构SNR-EQ-JSCC。该架构基于通用Transformer模型，通过将信噪比（SNR）嵌入注意力模块，并利用信道自适应查询动态调节注意力分数实现信道适应。同时，在损失函数中引入惩罚项以稳定训练过程。考虑到瞬时信噪比反馈可能不准确，本文还提出仅使用平均信噪比的替代方法，无需重新训练即可应用。仿真结果表明，所提SNR-EQ-JSCC在图像传输任务中，相比最先进的SwinJSCC，在峰值信噪比（PSNR）和感知指标上均有提升，且信道适应部分仅需0.05%的存储开销和6.38%的计算复杂度。此外，信道自适应查询显著提升了感知性能；当瞬时信噪比反馈不准确时，仅使用平均信噪比的版本仍优于基线方案。
原文摘要 · Abstract (English)
Coping with the impact of dynamic channels is a critical issue in joint source-channel coding (JSCC)-based semantic communication systems. In this paper, we propose a lightweight channel-adaptive semantic coding architecture called SNR-EQ-JSCC. It is built upon the generic Transformer model and achieves channel adaptation (CA) by Embedding the signal-to-noise ratio (SNR) into the attention blocks and dynamically adjusting attention scores through channel-adaptive Queries. Meanwhile, penalty terms are introduced in the loss function to stabilize the training process. Considering that instantaneous SNR feedback may be imperfect, we propose an alternative method that uses only the average SNR, which requires no retraining of SNR-EQ-JSCC. Simulation results conducted on image transmission demonstrate that the proposed SNR-EQJSCC outperforms the state-of-the-art SwinJSCC in peak signal-to-noise ratio (PSNR) and perception metrics while only requiring 0.05% of the storage overhead and 6.38% of the computational complexity for CA. Moreover, the channel-adaptive query method demonstrates significant improvements in perception metrics. When instantaneous SNR feedback is imperfect, SNR-EQ-JSCC using only the average SNR still surpasses baseline schemes.
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