# Information-Preserving CSI Feedback: Invertible Networks with Endogenous Quantization and Channel Error Mitigation

arXiv: https://arxiv.org/abs/2507.20283 · HTML: https://mlsanity.com/paper/2507.20283/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 36 references that are in the map.
- First posted on arXiv 27 Jul 2025.

## Builds on (most cited first, 18 of 18)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [Density estimation using Real NVP](https://mlsanity.com/paper/1605.08803/) (2016)
- [Glow: Generative Flow with Invertible 1x1 Convolutions](https://mlsanity.com/paper/1807.03039/) (2018)
- [Wasserstein Auto-Encoders](https://mlsanity.com/paper/1711.01558/) (2017)
- [Analyzing Inverse Problems with Invertible Neural Networks](https://mlsanity.com/paper/1808.04730/) (2018)
- [DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications](https://mlsanity.com/paper/1902.06435/) (2019)
- [HashNet: Deep Learning to Hash by Continuation](https://mlsanity.com/paper/1702.00758/) (2017)
- [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)
- [Deep Joint Source-Channel Coding for CSI Feedback: An End-to-End Approach](https://mlsanity.com/paper/2203.16005/) (2022)
- [Deep Learning based Denoise Network for CSI Feedback in FDD Massive MIMO Systems](https://mlsanity.com/paper/2004.07576/) (2020)
- [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)
- [Binarized Aggregated Network with Quantization: Flexible Deep Learning Deployment for CSI Feedback in Massive MIMO System](https://mlsanity.com/paper/2105.00354/) (2021)
- [Quantization Adaptor for Bit-Level Deep Learning-Based Massive MIMO CSI Feedback](https://mlsanity.com/paper/2211.02937/) (2022)
- [Downlink Channel Covariance Matrix Reconstruction for FDD Massive MIMO Systems with Limited Feedback](https://mlsanity.com/paper/2204.00779/) (2022)

## Abstract (arXiv)

Deep learning has emerged as a promising solution for efficient channel state information (CSI) feedback in frequency division duplex (FDD) massive MIMO systems. Conventional deep learning-based methods typically rely on a deep autoencoder to compress the CSI, which leads to irreversible information loss and degrades reconstruction accuracy. This paper introduces InvCSINet, an information-preserving CSI feedback framework based on invertible neural networks (INNs). By leveraging the bijective nature of INNs, the model ensures information-preserving compression and reconstruction with shared model parameters. To address practical challenges such as quantization and channel-induced errors, we endogenously integrate an adaptive quantization module, a differentiable bit-channel distortion module and an information compensation module into the INN architecture. This design enables the network to learn and compensate the information loss during CSI compression, quantization, and noisy transmission, thereby preserving the CSI integrity throughout the feedback process. Simulation results validate the effectiveness of the proposed scheme, demonstrating superior CSI recovery performance and robustness to practical impairments with a lightweight architecture.