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[2602.13556] Discrete-Space Generative AI Pipeline for Semantic Transmission of Signals
Summary
The paper presents 'Discernment,' a generative AI system designed for semantic communication, effectively transmitting physical signals while adapting to channel impairments.
Why It Matters
This research is significant as it addresses the challenges of maintaining semantic integrity in communication systems, particularly in IoT environments where channel conditions can vary greatly. The findings could lead to advancements in reliable signal processing and AI applications in real-world scenarios.
Key Takeaways
- Discernment adapts to channel impairments using generative algorithms.
- Maintains semantic integrity even under severe channel degradation.
- Demonstrates low model complexity and high spectral efficiency.
- Well-suited for IoT applications, enhancing communication reliability.
- Encourages further research in semantic channel paradigms.
Computer Science > Information Theory arXiv:2602.13556 (cs) [Submitted on 14 Feb 2026] Title:Discrete-Space Generative AI Pipeline for Semantic Transmission of Signals Authors:Silvija Kokalj-Filipovic, Yagna Kaasaragadda View a PDF of the paper titled Discrete-Space Generative AI Pipeline for Semantic Transmission of Signals, by Silvija Kokalj-Filipovic and Yagna Kaasaragadda View PDF HTML (experimental) Abstract: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. Subjects: Information Theory (cs.IT); Artificial Intelligence (cs.AI); Signal Processing (eess.SP) Cite as: arXiv:2602.13556 [cs.IT] (or arXi...
