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

arXiv: https://arxiv.org/abs/2407.00896 · HTML: https://mlsanity.com/paper/2407.00896/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 12 references that are in the map.
- First posted on arXiv 1 Jul 2024.

## Builds on (most cited first, 3 of 3)

- [CAnet: Uplink-aided Downlink Channel Acquisition in FDD Massive MIMO using Deep Learning](https://mlsanity.com/paper/2101.04377/) (2021)
- [AI for CSI Feedback Enhancement in 5G-Advanced](https://mlsanity.com/paper/2206.15132/) (2022)
- [Knowledge-driven Meta-learning for CSI Feedback](https://mlsanity.com/paper/2310.15548/) (2023)

## Built on by (most cited first, 4 of 4)

- [Augmenting Channel Simulator and Semi- Supervised Learning for Efficient Indoor Positioning](https://mlsanity.com/paper/2408.00429/) (2024)
- [Denoising and Augmentation: A Dual Use of Diffusion Model for Enhanced CSI Recovery](https://mlsanity.com/paper/2510.02744/) (2025)
- [Score-Based Conditional Flow Models for MIMO Receiver Design with Superimposed Pilots](https://mlsanity.com/paper/2602.21654/) (2026)
- [Learnware for CSI Feedback: Scene-specific Small Models Can Do Big](https://mlsanity.com/paper/2608.17760/) (2026)

## Abstract (arXiv)

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.