# Prompt-Enabled Large AI Models for CSI Feedback

arXiv: https://arxiv.org/abs/2501.10629 · HTML: https://mlsanity.com/paper/2501.10629/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jan 2025.

## Built on by (most cited first, 6 of 6)

- [LVM4CSI: Enabling Direct Application of Pre-Trained Large Vision Models for Wireless Channel Tasks](https://mlsanity.com/paper/2507.05121/) (2025)
- [Large AI Models for Wireless Physical Layer](https://mlsanity.com/paper/2508.02314/) (2025)
- [Generative Diffusion Receivers: Achieving Pilot-Efficient MIMO-OFDM Communications](https://mlsanity.com/paper/2506.18419/) (2025)
- [A Multi-Modal Foundational Model for Wireless Communication and Sensing](https://mlsanity.com/paper/2602.04016/) (2026)
- [BERT4beam: Large AI Model Enabled Generalized Beamforming Optimization](https://mlsanity.com/paper/2509.11056/) (2025)
- [Learnware for CSI Feedback: Scene-specific Small Models Can Do Big](https://mlsanity.com/paper/2608.17760/) (2026)

## Abstract (arXiv)

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.