Summary of Semantic Successive Refinement: a Generative Ai-aided Semantic Communication Framework, by Kexin Zhang et al.
Semantic Successive Refinement: A Generative AI-aided Semantic Communication Framework
by Kexin Zhang, Lixin Li, Wensheng Lin, Yuna Yan, Rui Li, Wenchi Cheng, Zhu Han
First submitted to arxiv on: 31 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Image and Video Processing (eess.IV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel generative AI semantic communication system that surpasses the Shannon limit, particularly in low Signal-to-Noise Ratio (SNR) environments. The system employs a joint source-channel coding mechanism based on the Swin Transformer for efficient semantic feature extraction and compression. At the receiver end, an advanced Diffusion Model reconstructs high-quality images from degraded signals, enhancing perceptual details. The authors also introduce a Multi-User Generative Semantic Communication (MU-GSC) system utilizing an asynchronous processing model that manages multiple user requests and optimizes system resources for parallel processing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, the paper develops new ways to send information through noisy channels while keeping the quality of what’s being sent high. The method uses special AI models to compress data at the sender side and reconstruct it correctly at the receiver side. This approach can help improve communication efficiency and content quality in various channel conditions. |
Keywords
* Artificial intelligence * Diffusion model * Feature extraction * Transformer
