Discrete-Space Generative AI Pipeline for Semantic Transmission of Signals
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| _version_ | 1866917274607157248 |
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| author | Kokalj-Filipovic, Silvija Kaasaragadda, Yagna |
| author_facet | Kokalj-Filipovic, Silvija Kaasaragadda, Yagna |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_13556 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Discrete-Space Generative AI Pipeline for Semantic Transmission of Signals Kokalj-Filipovic, Silvija Kaasaragadda, Yagna Information Theory Artificial Intelligence Signal Processing 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. |
| title | Discrete-Space Generative AI Pipeline for Semantic Transmission of Signals |
| topic | Information Theory Artificial Intelligence Signal Processing |
| url | https://arxiv.org/abs/2602.13556 |