# AnciNet: An Efficient Deep Learning Approach for Feedback Compression of Estimated CSI in Massive MIMO Systems

arXiv: https://arxiv.org/abs/2008.07112 · HTML: https://mlsanity.com/paper/2008.07112/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Aug 2020.

## Built on by (most cited first, 7 of 7)

- [Overview of Deep Learning-based CSI Feedback in Massive MIMO Systems](https://mlsanity.com/paper/2206.14383/) (2022)
- [Binarized Aggregated Network with Quantization: Flexible Deep Learning Deployment for CSI Feedback in Massive MIMO System](https://mlsanity.com/paper/2105.00354/) (2021)
- [Knowledge and Data Dual-Driven Channel Estimation and Feedback for Ultra-Massive MIMO Systems under Hybrid Field Beam Squint Effect](https://mlsanity.com/paper/2403.12813/) (2024)
- [Aggregated Network for Massive MIMO CSI Feedback](https://mlsanity.com/paper/2101.06618/) (2021)
- [Joint Channel Estimation and Feedback with Masked Token Transformers in Massive MIMO Systems](https://mlsanity.com/paper/2306.06125/) (2023)
- [Self-information Domain-based Neural CSI Compression with Feature Coupling](https://mlsanity.com/paper/2305.07662/) (2023)
- [Study on Downlink CSI compression: Are Neural Networks the Only Solution?](https://mlsanity.com/paper/2502.17459/) (2025)

## Abstract (arXiv)

Accurate channel state information (CSI) feedback plays a vital role in improving the performance gain of massive multiple-input multiple-output (m-MIMO) systems, where the dilemma is excessive CSI overhead versus limited feedback bandwith. By considering the noisy CSI due to imperfect channel estimation, we propose a novel deep neural network architecture, namely AnciNet, to conduct the CSI feedback with limited bandwidth. AnciNet extracts noise-free features from the noisy CSI samples to achieve effective CSI compression for the feedback. Experimental results verify that the proposed AnciNet approach outperforms the existing techniques under various conditions.