# Quantization Design for Deep Learning-Based CSI Feedback

arXiv: https://arxiv.org/abs/2503.08125 · HTML: https://mlsanity.com/paper/2503.08125/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 21 references that are in the map.
- First posted on arXiv 11 Mar 2025.

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

- [Straightening Out the Straight-Through Estimator: Overcoming Optimization Challenges in Vector Quantized Networks](https://mlsanity.com/paper/2305.08842/) (2023)
- [Overview of Deep Learning-based CSI Feedback in Massive MIMO Systems](https://mlsanity.com/paper/2206.14383/) (2022)
- [Convolutional Neural Network based Multiple-Rate Compressive Sensing for Massive MIMO CSI Feedback: Design, Simulation, and Analysis](https://mlsanity.com/paper/1906.06007/) (2019)
- [Multi-resolution CSI Feedback with deep learning in Massive MIMO System](https://mlsanity.com/paper/1910.14322/) (2019)
- [Distributed Deep Convolutional Compression for Massive MIMO CSI Feedback](https://mlsanity.com/paper/2003.04684/) (2020)
- [Fast Exact k-Means, k-Medians and Bregman Divergence Clustering in 1D](https://mlsanity.com/paper/1701.07204/) (2017)
- [An Efficient Deep Learning Framework for Low Rate Massive MIMO CSI Reporting](https://mlsanity.com/paper/1912.10608/) (2019)
- [Changeable Rate and Novel Quantization for CSI Feedback Based on Deep Learning](https://mlsanity.com/paper/2202.13627/) (2022)
- [Deep CSI Compression for Massive MIMO: A Self-information Model-driven Neural Network](https://mlsanity.com/paper/2204.11567/) (2022)
- [Machine Learning-Based CSI Feedback With Variable Length in FDD Massive MIMO](https://mlsanity.com/paper/2204.04723/) (2022)
- [A Learnable Optimization and Regularization Approach to Massive MIMO CSI Feedback](https://mlsanity.com/paper/2209.15340/) (2022)
- [CSI-PPPNet: A One-Sided One-for-All Deep Learning Framework for Massive MIMO CSI Feedback](https://mlsanity.com/paper/2211.15851/) (2022)

## Abstract (arXiv)

Deep learning-based autoencoders have been employed to compress and reconstruct channel state information (CSI) in frequency-division duplex systems. Practical implementations require judicious quantization of encoder outputs for digital transmission. In this paper, we propose a novel quantization module with bit allocation among encoder outputs and develop a method for joint training the module and the autoencoder. To enhance learning performance, we design a loss function that adaptively weights the quantization loss and the logarithm of reconstruction loss. Simulation results show the performance gain of the proposed method over existing baselines.