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.

翻译：本文提出Discernment系统，这是一种基于离散空间生成式人工智能模型的语义通信系统，能够在技术信道中传输物理信号（基带无线电与音频）的语义信息。Discernment通过根据擦除模式在自回归与基于扩散的生成算法之间动态切换，自适应地应对信道损伤（建模为擦除信道）。实验结果表明，即使在信道容量严重恶化的情况下，Discernment仍能保持语义完整性，在分类准确率和重建语义的统计保真度两方面均表现出极小且平缓的性能下降。这些发现证明了Discernment在保持频谱效率和低模型复杂度的同时，能够适应多样化的物理信道条件，使其非常适用于物联网部署，并为该语义信道范式的深入研究提供了有力依据。