Prompt-Enabled Large AI Models for CSI Feedback

TL;DR

This paper introduces a prompt-enabled large AI model with transformer architecture for CSI feedback, leveraging environmental knowledge to improve accuracy and generalization across diverse datasets.

Contribution

The paper proposes a novel prompt-enabled large AI model that incorporates environmental knowledge as prompts, enhancing CSI feedback performance and generalization.

Findings

Significant improvement in feedback accuracy.

Enhanced generalization across diverse scenarios.

Reduced data collection needs for new environments.

Abstract

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.