# Dig-CSI: A Distributed and Generative Model Assisted CSI Feedback Training Framework

arXiv: https://arxiv.org/abs/2312.05921 · HTML: https://mlsanity.com/paper/2312.05921/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 25 references that are in the map.
- First posted on arXiv 10 Dec 2023.

## Builds on (most cited first, 12 of 12)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications](https://mlsanity.com/paper/2001.06937/) (2020)
- [Sliced Wasserstein Generative Models](https://mlsanity.com/paper/1904.05408/) (2019)
- [Overview of Deep Learning-based CSI Feedback in Massive MIMO Systems](https://mlsanity.com/paper/2206.14383/) (2022)
- [Multi-resolution CSI Feedback with deep learning in Massive MIMO System](https://mlsanity.com/paper/1910.14322/) (2019)
- [CLNet: Complex Input Lightweight Neural Network designed for Massive MIMO CSI Feedback](https://mlsanity.com/paper/2102.07507/) (2021)
- [PRVNet: A Novel Partially-Regularized Variational Autoencoders for Massive MIMO CSI Feedback](https://mlsanity.com/paper/2011.04178/) (2020)
- [A Spatially Separable Attention Mechanism for massive MIMO CSI Feedback](https://mlsanity.com/paper/2208.03369/) (2022)
- [Binarized Aggregated Network with Quantization: Flexible Deep Learning Deployment for CSI Feedback in Massive MIMO System](https://mlsanity.com/paper/2105.00354/) (2021)
- [Machine Learning-Based 3D Channel Modeling for U2V mmWave Communications](https://mlsanity.com/paper/2109.02104/) (2021)
- [Communication-efficient Personalized Federated Edge Learning for Massive MIMO CSI Feedback](https://mlsanity.com/paper/2303.13782/) (2023)
- [Channel Modeling for UAV-to-Ground Communications with Posture Variation and Fuselage Scattering Effect](https://mlsanity.com/paper/2210.02245/) (2022)

## Built on by (most cited first, 1 of 1)

- [Distributed Gossip-GAN for Low-overhead CSI Feedback Training in FDD mMIMO-OFDM Systems](https://mlsanity.com/paper/2509.10490/) (2025)

## Abstract (arXiv)

The advent of deep learning (DL)-based models has significantly advanced Channel State Information (CSI) feedback mechanisms in wireless communication systems. However, traditional approaches often suffer from high communication overhead and potential privacy risks due to the centralized nature of CSI data processing. To address these challenges, we design a CSI feedback training framework called Dig-CSI, in which the dataset for training the CSI feedback model is produced by the distributed generators uploaded by each user equipment (UE), but not through local data upload. Each UE trains an autoencoder, where the decoder is considered as the distributed generator, with local data to gain reconstruction accuracy and the ability to generate. Experimental results show that Dig-CSI can train a global CSI feedback model with comparable performance to the model trained with classical centralized learning with a much lighter communication overhead.