# Learning-Based Phase Compression and Quantization for Massive MIMO CSI Feedback with Magnitude-Aided Information

arXiv: https://arxiv.org/abs/2103.00432 · HTML: https://mlsanity.com/paper/2103.00432/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2021.

## Built on by (most cited first, 6 of 6)

- [Overview of Deep Learning-based CSI Feedback in Massive MIMO Systems](https://mlsanity.com/paper/2206.14383/) (2022)
- [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)
- [Deep Learning Empowered Type-II Codebook: New Paradigm for Enhancing CSI Feedback](https://mlsanity.com/paper/2305.08081/) (2023)
- [Physics-Inspired Deep Learning Anti-Aliasing Framework in Efficient Channel State Feedback](https://mlsanity.com/paper/2403.08133/) (2024)
- [Study on Downlink CSI compression: Are Neural Networks the Only Solution?](https://mlsanity.com/paper/2502.17459/) (2025)
- [Uplink Assisted Joint Channel Estimation and CSI Feedback: An Approach Based on Deep Joint Source-Channel Coding](https://mlsanity.com/paper/2504.10836/) (2025)

## Abstract (arXiv)

Massive MIMO wireless FDD systems are often confronted by the challenge to efficiently obtain downlink channel state information (CSI). Previous works have demonstrated the potential in CSI encoding and recovery by take advantage of uplink/downlink reciprocity between their CSI magnitudes. However, such a framework separately encodes CSI phase and magnitude. To improve CSI encoding, we propose a learning-based framework based on limited CSI feedback and magnitude-aided information. Moving beyond previous works, our proposed framework with a modified loss function enables end-to-end learning to jointly optimize the CSI magnitude and phase recovery performance. Simulations show that the framework outperforms alternate approaches for phase recovery over overall CSI recovery in indoor and outdoor scenarios.