# Quantization Adaptor for Bit-Level Deep Learning-Based Massive MIMO CSI Feedback

arXiv: https://arxiv.org/abs/2211.02937 · HTML: https://mlsanity.com/paper/2211.02937/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 38 references that are in the map.
- First posted on arXiv 5 Nov 2022.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices](https://mlsanity.com/paper/1707.01083/) (2017)
- [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)
- [Lightweight Convolutional Neural Networks for CSI Feedback in Massive MIMO](https://mlsanity.com/paper/2005.00438/) (2020)
- [An Efficient Deep Learning Framework for Low Rate Massive MIMO CSI Reporting](https://mlsanity.com/paper/1912.10608/) (2019)
- [Compression and Acceleration of Neural Networks for Communications](https://mlsanity.com/paper/1907.13269/) (2019)
- [Bit-level Optimized Neural Network for Multi-antenna Channel Quantization](https://mlsanity.com/paper/1909.10730/) (2019)
- [Binary Neural Network Aided CSI Feedback in Massive MIMO System](https://mlsanity.com/paper/2011.02692/) (2020)
- [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)
- [Learning Representations for CSI Adaptive Quantization and Feedback](https://mlsanity.com/paper/2207.06924/) (2022)

## Built on by (most cited first, 3 of 3)

- [Vector Quantization for Deep-Learning-Based CSI Feedback in Massive MIMO Systems](https://mlsanity.com/paper/2403.07355/) (2024)
- [Information-Preserving CSI Feedback: Invertible Networks with Endogenous Quantization and Channel Error Mitigation](https://mlsanity.com/paper/2507.20283/) (2025)
- [A Wireless World Model for AI-Native 6G Networks](https://mlsanity.com/paper/2603.25216/) (2026)

## Abstract (arXiv)

In massive multiple-input multiple-output (MIMO) systems, the user equipment (UE) needs to feed the channel state information (CSI) back to the base station (BS) for the following beamforming. But the large scale of antennas in massive MIMO systems causes huge feedback overhead. Deep learning (DL) based methods can compress the CSI at the UE and recover it at the BS, which reduces the feedback cost significantly. But the compressed CSI must be quantized into bit streams for transmission. In this paper, we propose an adaptor-assisted quantization strategy for bit-level DL-based CSI feedback. First, we design a network-aided adaptor and an advanced training scheme to adaptively improve the quantization and reconstruction accuracy. Moreover, for easy practical employment, we introduce the expert knowledge of data distribution and propose a pluggable and cost-free adaptor scheme. Experiments show that compared with the state-of-the-art feedback quantization method, this adaptor-aided quantization strategy can achieve better quantization accuracy and reconstruction performance with less or no additional cost. The open-source codes are available at https://github.com/zhang-xd18/QCRNet.