# Overview of Deep Learning-based CSI Feedback in Massive MIMO Systems

arXiv: https://arxiv.org/abs/2206.14383 · HTML: https://mlsanity.com/paper/2206.14383/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- Builds on 75 of its 183 references that are in the map.
- First posted on arXiv 29 Jun 2022.

## Builds on (most cited first, 30 of 75)

- [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)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://mlsanity.com/paper/1502.03044/) (2015)
- [ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices](https://mlsanity.com/paper/1707.01083/) (2017)
- [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size](https://mlsanity.com/paper/1602.07360/) (2016)
- [HyperNetworks](https://mlsanity.com/paper/1609.09106/) (2016)
- [Variational image compression with a scale hyperprior](https://mlsanity.com/paper/1802.01436/) (2018)
- [Exploring Self-attention for Image Recognition](https://mlsanity.com/paper/2004.13621/) (2020)
- [Slimmable Neural Networks](https://mlsanity.com/paper/1812.08928/) (2018)
- [An Introduction to Deep Learning for the Physical Layer](https://mlsanity.com/paper/1702.00832/) (2017)
- [Wider or Deeper: Revisiting the ResNet Model for Visual Recognition](https://mlsanity.com/paper/1611.10080/) (2016)
- [ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks](https://mlsanity.com/paper/1908.03930/) (2019)
- [Scaling Up Your Kernels to 31x31: Revisiting Large Kernel Design in CNNs](https://mlsanity.com/paper/2203.06717/) (2022)
- [Model-Based Deep Learning](https://mlsanity.com/paper/2012.08405/) (2020)
- [DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications](https://mlsanity.com/paper/1902.06435/) (2019)
- [Understanding LSTM -- a tutorial into Long Short-Term Memory Recurrent Neural Networks](https://mlsanity.com/paper/1909.09586/) (2019)
- [Deep Learning-Based Communication Over the Air](https://mlsanity.com/paper/1707.03384/) (2017)
- [Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent](https://mlsanity.com/paper/1802.01737/) (2018)
- [Toward a 6G AI-Native Air Interface](https://mlsanity.com/paper/2012.08285/) (2020)
- [Model-Driven Deep Learning for Physical Layer Communications](https://mlsanity.com/paper/1809.06059/) (2018)
- [Convolutional Neural Network based Multiple-Rate Compressive Sensing for Massive MIMO CSI Feedback: Design, Simulation, and Analysis](https://mlsanity.com/paper/1906.06007/) (2019)
- [EfficientFi: Towards Large-Scale Lightweight WiFi Sensing via CSI Compression](https://mlsanity.com/paper/2204.04138/) (2022)
- [Multi-resolution CSI Feedback with deep learning in Massive MIMO System](https://mlsanity.com/paper/1910.14322/) (2019)
- [Deep Learning for Distributed Channel Feedback and Multiuser Precoding in FDD Massive MIMO](https://mlsanity.com/paper/2007.06512/) (2020)

## Built on by (most cited first, 30 of 37)

- [Pay Less But Get More: A Dual-Attention-based Channel Estimation Network for Massive MIMO Systems with Low-Density Pilots](https://mlsanity.com/paper/2303.00986/) (2023)
- [LVM4CSI: Enabling Direct Application of Pre-Trained Large Vision Models for Wireless Channel Tasks](https://mlsanity.com/paper/2507.05121/) (2025)
- [CSI-GPT: Integrating Generative Pre-Trained Transformer with Federated-Tuning to Acquire Downlink Massive MIMO Channels](https://mlsanity.com/paper/2406.03438/) (2024)
- [AI for CSI Feedback Enhancement in 5G-Advanced](https://mlsanity.com/paper/2206.15132/) (2022)
- [Spectral Temporal Graph Neural Network for massive MIMO CSI Prediction](https://mlsanity.com/paper/2312.02159/) (2023)
- [Generative Diffusion Model-based Compression of MIMO CSI](https://mlsanity.com/paper/2503.03753/) (2025)
- [Generative Diffusion Receivers: Achieving Pilot-Efficient MIMO-OFDM Communications](https://mlsanity.com/paper/2506.18419/) (2025)
- [Precoding-oriented Massive MIMO CSI Feedback Design](https://mlsanity.com/paper/2302.11526/) (2023)
- [Streamlining Multimodal Data Fusion in Wireless Communication and Sensor Networks](https://mlsanity.com/paper/2302.12636/) (2023)
- [Design of JiuTian Intelligent Network Simulation Platform](https://mlsanity.com/paper/2310.06858/) (2023)
- [AI/ML Life Cycle Management for Interoperable AI Native RAN](https://mlsanity.com/paper/2507.18538/) (2025)
- [Improving the Performance of R17 Type-II Codebook with Deep Learning](https://mlsanity.com/paper/2310.05962/) (2023)
- [Auto-CsiNet: Scenario-customized Automatic Neural Network Architecture Generation for Massive MIMO CSI Feedback](https://mlsanity.com/paper/2311.15950/) (2023)
- [Dig-CSI: A Distributed and Generative Model Assisted CSI Feedback Training Framework](https://mlsanity.com/paper/2312.05921/) (2023)
- [CSI Compression using Channel Charting](https://mlsanity.com/paper/2501.01431/) (2024)
- [Residual Cross-Attention Transformer-Based Multi-User CSI Feedback with Deep Joint Source-Channel Coding](https://mlsanity.com/paper/2505.19465/) (2025)
- [Demonstrating Interoperable Channel State Feedback Compression with Machine Learning](https://mlsanity.com/paper/2506.21796/) (2025)
- [Random Multiplexing](https://mlsanity.com/paper/2512.24087/) (2025)
- [Emulators in JINSP](https://mlsanity.com/paper/2311.16146/) (2023)
- [Implement services for business scenarios by combining basic emulators](https://mlsanity.com/paper/2312.08815/) (2023)
- [Universal Auto-encoder Framework for MIMO CSI Feedback](https://mlsanity.com/paper/2403.00299/) (2024)
- [Online Optimization for Learning to Communicate over Time-Correlated Channels](https://mlsanity.com/paper/2409.00575/) (2024)
- [Improved Variational Inference in Discrete VAEs using Error Correcting Codes](https://mlsanity.com/paper/2410.07840/) (2024)
- [Quantization Design for Deep Learning-Based CSI Feedback](https://mlsanity.com/paper/2503.08125/) (2025)
- [Uplink Assisted Joint Channel Estimation and CSI Feedback: An Approach Based on Deep Joint Source-Channel Coding](https://mlsanity.com/paper/2504.10836/) (2025)
- [Information-Preserving CSI Feedback: Invertible Networks with Endogenous Quantization and Channel Error Mitigation](https://mlsanity.com/paper/2507.20283/) (2025)
- [Way to Build Native AI-driven 6G Air Interface: Principles, Roadmap, and Outlook](https://mlsanity.com/paper/2508.15277/) (2025)
- [Foundation Model-Based Adaptive Semantic Image Transmission for Dynamic Wireless Environments](https://mlsanity.com/paper/2509.23590/) (2025)
- [Precoding-Oriented CSI Feedback Design with Mutual Information Regularized VQ-VAE](https://mlsanity.com/paper/2602.02508/) (2026)
- [Fundamental Limits of CSI Compression in FDD Massive MIMO](https://mlsanity.com/paper/2603.14325/) (2026)

## Abstract (arXiv)

Many performance gains achieved by massive multiple-input and multiple-output depend on the accuracy of the downlink channel state information (CSI) at the transmitter (base station), which is usually obtained by estimating at the receiver (user terminal) and feeding back to the transmitter. The overhead of CSI feedback occupies substantial uplink bandwidth resources, especially when the number of the transmit antennas is large. Deep learning (DL)-based CSI feedback refers to CSI compression and reconstruction by a DL-based autoencoder and can greatly reduce feedback overhead. In this paper, a comprehensive overview of state-of-the-art research on this topic is provided, beginning with basic DL concepts widely used in CSI feedback and then categorizing and describing some existing DL-based feedback works. The focus is on novel neural network architectures and utilization of communication expert knowledge to improve CSI feedback accuracy. Works on bit-level CSI feedback and joint design of CSI feedback with other communication modules are also introduced, and some practical issues, including training dataset collection, online training, complexity, generalization, and standardization effect, are discussed. At the end of the paper, some challenges and potential research directions associated with DL-based CSI feedback in future wireless communication systems are identified.