# An Efficient Network with Novel Quantization Designed for Massive MIMO CSI Feedback

arXiv: https://arxiv.org/abs/2405.20068 · HTML: https://mlsanity.com/paper/2405.20068/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 20 references that are in the map.
- First posted on arXiv 30 May 2024.

## Builds on (most cited first, 10 of 10)

- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [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)
- [CLNet: Complex Input Lightweight Neural Network designed for Massive MIMO CSI Feedback](https://mlsanity.com/paper/2102.07507/) (2021)
- [Dilated Convolution based CSI Feedback Compression for Massive MIMO Systems](https://mlsanity.com/paper/2106.04043/) (2021)
- [Changeable Rate and Novel Quantization for CSI Feedback Based on Deep Learning](https://mlsanity.com/paper/2202.13627/) (2022)
- [Towards Massive MIMO 2.0: Understanding spatial correlation, interference suppression, and pilot contamination](https://mlsanity.com/paper/1904.03406/) (2019)
- [Learning Representations for CSI Adaptive Quantization and Feedback](https://mlsanity.com/paper/2207.06924/) (2022)
- [A Learnable Optimization and Regularization Approach to Massive MIMO CSI Feedback](https://mlsanity.com/paper/2209.15340/) (2022)

## Built on by (most cited first, 1 of 1)

- [Generative Diffusion Model-based Compression of MIMO CSI](https://mlsanity.com/paper/2503.03753/) (2025)

## Abstract (arXiv)

The efficacy of massive multiple-input multiple-output (MIMO) techniques heavily relies on the accuracy of channel state information (CSI) in frequency division duplexing (FDD) systems. Many works focus on CSI compression and quantization methods to enhance CSI reconstruction accuracy with lower feedback overhead. In this letter, we propose CsiConformer, a novel CSI feedback network that combines convolutional operations and self-attention mechanisms to improve CSI feedback accuracy. Additionally, a new quantization module is developed to improve encoding efficiency. Experiment results show that CsiConformer outperforms previous state-of-the-art networks, achieving an average accuracy improvement of 17.67% with lower computational overhead.