Channel Modeling Aided Dataset Generation for AI-Enabled CSI Feedback: Advances, Challenges, and Solutions

TL;DR

This paper introduces a channel modeling aided data augmentation approach for CSI feedback that leverages limited real data to generate extensive datasets, improving AI model performance in FDD MIMO systems.

Contribution

It proposes a novel method combining field data with updated channel models to generate training datasets, addressing practical deployment challenges of AI-based CSI feedback.

Findings

Significant performance improvement over benchmarks

Effective use of limited field data for dataset generation

Enhanced model generalization and monitoring

Abstract

The AI-enabled autoencoder has demonstrated great potential in channel state information (CSI) feedback in frequency division duplex (FDD) multiple input multiple output (MIMO) systems. However, this method completely changes the existing feedback strategies, making it impractical to deploy in recent years. To address this issue, this paper proposes a channel modeling aided data augmentation method based on a limited number of field channel data. Specifically, the user equipment (UE) extracts the primary stochastic parameters of the field channel data and transmits them to the base station (BS). The BS then updates the typical TR 38.901 model parameters with the extracted parameters. In this way, the updated channel model is used to generate the dataset. This strategy comprehensively considers the dataset collection, model generalization, model monitoring, and so on. Simulations verify that our proposed strategy can significantly improve performance compared to the benchmarks.