Prompt-Enabled Large AI Models for CSI Feedback
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910906860961792 |
|---|---|
| author | Guo, Jiajia Cui, Yiming Wen, Chao-Kai Jin, Shi |
| author_facet | Guo, Jiajia Cui, Yiming Wen, Chao-Kai Jin, Shi |
| contents | Artificial intelligence (AI) has emerged as a promising tool for channel state information (CSI) feedback. While recent research primarily focuses on improving feedback accuracy on a specific dataset through novel architectures, the underlying mechanism of AI-based CSI feedback remains unclear. This study explores the mechanism through analyzing performance across diverse datasets, with findings suggesting that superior feedback performance stems from AI models' strong fitting capabilities and their ability to leverage environmental knowledge. Building on these findings, we propose a prompt enabled large AI model (LAM) for CSI feedback. The LAM employs powerful transformer blocks and is trained on extensive datasets from various scenarios. Meanwhile, the channel distribution (environmental knowledge) -- represented as the mean of channel magnitude in the angular-delay domain -- is incorporated as a prompt within the decoder to further enhance reconstruction quality. Simulation results confirm that the proposed prompt-enabled LAM significantly improves feedback accuracy and generalization performance while reducing data collection requirements in new scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_10629 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Prompt-Enabled Large AI Models for CSI Feedback Guo, Jiajia Cui, Yiming Wen, Chao-Kai Jin, Shi Information Theory Signal Processing Artificial intelligence (AI) has emerged as a promising tool for channel state information (CSI) feedback. While recent research primarily focuses on improving feedback accuracy on a specific dataset through novel architectures, the underlying mechanism of AI-based CSI feedback remains unclear. This study explores the mechanism through analyzing performance across diverse datasets, with findings suggesting that superior feedback performance stems from AI models' strong fitting capabilities and their ability to leverage environmental knowledge. Building on these findings, we propose a prompt enabled large AI model (LAM) for CSI feedback. The LAM employs powerful transformer blocks and is trained on extensive datasets from various scenarios. Meanwhile, the channel distribution (environmental knowledge) -- represented as the mean of channel magnitude in the angular-delay domain -- is incorporated as a prompt within the decoder to further enhance reconstruction quality. Simulation results confirm that the proposed prompt-enabled LAM significantly improves feedback accuracy and generalization performance while reducing data collection requirements in new scenarios. |
| title | Prompt-Enabled Large AI Models for CSI Feedback |
| topic | Information Theory Signal Processing |
| url | https://arxiv.org/abs/2501.10629 |