# Deep Convolutional Compression for Massive MIMO CSI Feedback

arXiv: https://arxiv.org/abs/1907.02942 · HTML: https://mlsanity.com/paper/1907.02942/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2019.

## Built on by (most cited first, 9 of 9)

- [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)
- [Aggregated Network for Massive MIMO CSI Feedback](https://mlsanity.com/paper/2101.06618/) (2021)
- [Federated Neural Compression Under Heterogeneous Data](https://mlsanity.com/paper/2305.16416/) (2023)
- [An Adaptive CSI Feedback Model Based on BiLSTM for Massive MIMO-OFDM Systems](https://mlsanity.com/paper/2408.06359/) (2024)
- [Tensor Learning-based Precoder Codebooks for FD-MIMO Systems](https://mlsanity.com/paper/2106.11374/) (2021)
- [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)
- [Way to Build Native AI-driven 6G Air Interface: Principles, Roadmap, and Outlook](https://mlsanity.com/paper/2508.15277/) (2025)

## Abstract (arXiv)

Coded caching provides significant gains over conventional uncoded caching by creating multicasting opportunities among distinct requests. Massive multiple-input multiple-output (MIMO) systems require downlink channel state information (CSI) at the base station (BS) to better utilize the available spatial diversity and multiplexing gains. However, in a frequency division duplex (FDD) massive MIMO system, the huge CSI feedback overhead becomes restrictive and degrades the overall spectral efficiency. In this paper, we propose a deep learning based channel state matrix compression scheme, called DeepCMC, composed of convolutional layers followed by quantization and entropy coding blocks. In comparison with previous works, the main contributions of DeepCMC are two-fold: i) DeepCMC is fully convolutional, and it can be used in a wide range of scenarios with various numbers of sub-channels and transmit antennas; ii) DeepCMC includes quantization and entropy coding blocks and minimizes a cost function that accounts for both the rate of compression and the reconstruction quality of the channel matrix at the BS. Simulation results demonstrate that DeepCMC significantly outperforms the state of the art compression schemes in terms of the reconstruction quality of the channel state matrix for the same compression rate, measured in bits per channel dimension.