Generative Network Layer for Communication Systems with Artificial Intelligence
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Abstract (arXiv)
The traditional role of the network layer is the transfer of packet replicas from source to destination through intermediate network nodes. We present a generative network layer that uses Generative AI (GenAI) at intermediate or edge network nodes and analyze its impact on the required data rates in the network. We conduct a case study where the GenAI-aided nodes generate images from prompts that consist of substantially compressed latent representations. The results from network flow analyses under image quality constraints show that the generative network layer can achieve an improvement of more than 100% in terms of the required data rate.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 9 of its 14 references that are in the map. First posted on arXiv 8 Dec 2023.
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9 of 14 references.
References · 9
- 2023GPT-4 Technical Reportcited by 18,396
- 2014Microsoft COCO: Common Objects in Contextcited by 17,608
- 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,355
- 2021On the Opportunities and Risks of Foundation Modelscited by 4,033
- 2022Imagen Video: High Definition Video Generation with Diffusion Modelscited by 1,778
- 2020High-Fidelity Generative Image Compressioncited by 338
- 2019Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoffcited by 211
- 2022Transframer: Arbitrary Frame Prediction with Generative Modelscited by 37
- 2023Making Sense of Meaning: A Survey on Metrics for Semantic and Goal-Oriented Communicationcited by 10
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Citing papers per year: 2025: 1.
1 paper.