用生成模型在离散空间传输信号语义，自适应应对信道损坏。
Discrete-Space Generative AI Pipeline for Semantic Transmission of Signals
- 基于离散空间的生成模型，根据信道丢包模式切换自回归或扩散算法。
- 信道容量大幅下降时，语义准确率和重构统计特性仍保持稳定。
- 适合物联网部署，兼具高谱效与低模型复杂度，可推广至更多场景。
我们提出Discernment，一种语义通信系统，利用在离散空间运行的生成式AI模型，将基带无线电和音频等物理信号的语义信息通过技术信道传输。Discernment能动态适应信道损伤——建模为擦除信道——根据擦除模式切换自回归或扩散生成算法。实验表明，即使信道容量严重降低，Discernment仍能保持语义完整性，分类准确率和重构语义的统计保真度仅出现微小且平滑的下降。这些结果证明了Discernment在多种物理信道条件下具备自适应能力，同时维持高谱效与低模型复杂度，非常适合物联网部署，并强烈激励对这一语义信道范式进一步研究。
原文摘要 · Abstract (English)
We introduce Discernment, a semantic communication system that transmits the meaning of physical signals (baseband radio and audio) over a technical channel using GenAI models operating in discrete spaces. Discernment dynamically adapts to channel impairments - modeled as erasure channels - by switching between an autoregressive or a diffusion-based generative algorithm, depending on the erasure pattern. Our results show that Discernment maintains semantic integrity even as channel capacity severely degrades, exhibiting very small and graceful performance decline in both classification accuracy and statistical fidelity of the reconstructed meaning. These findings demonstrate Discernment's ability to adjust to diverse physical channel conditions while maintaining spectral efficiency and low model complexity, making it well suited for IoT deployments and strongly motivating further research on this semantic channel paradigm.
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