# Deep Learning-based Implicit CSI Feedback in Massive MIMO

arXiv: https://arxiv.org/abs/2105.10100 · HTML: https://mlsanity.com/paper/2105.10100/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 48 references that are in the map.
- First posted on arXiv 21 May 2021.

## Builds on (most cited first, 12 of 12)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Lossy Image Compression with Compressive Autoencoders](https://mlsanity.com/paper/1703.00395/) (2017)
- [Deep Learning-Based Channel Estimation](https://mlsanity.com/paper/1810.05893/) (2018)
- [Deep Learning Methods for Improved Decoding of Linear Codes](https://mlsanity.com/paper/1706.07043/) (2017)
- [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)
- [Deep Transfer Learning Based Downlink Channel Prediction for FDD Massive MIMO Systems](https://mlsanity.com/paper/1912.12265/) (2019)
- [Deep Learning based Downlink Channel Prediction for FDD Massive MIMO System](https://mlsanity.com/paper/1908.03360/) (2019)
- [Deep Learning based Denoise Network for CSI Feedback in FDD Massive MIMO Systems](https://mlsanity.com/paper/2004.07576/) (2020)
- [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)
- [Spatio-Temporal Representation with Deep Neural Recurrent Network in MIMO CSI Feedback](https://mlsanity.com/paper/1908.07934/) (2019)

## Built on by (most cited first, 12 of 12)

- [Overview of Deep Learning-based CSI Feedback in Massive MIMO Systems](https://mlsanity.com/paper/2206.14383/) (2022)
- [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)
- [AI for CSI Feedback Enhancement in 5G-Advanced](https://mlsanity.com/paper/2206.15132/) (2022)
- [Precoding-oriented Massive MIMO CSI Feedback Design](https://mlsanity.com/paper/2302.11526/) (2023)
- [Knowledge and Data Dual-Driven Channel Estimation and Feedback for Ultra-Massive MIMO Systems under Hybrid Field Beam Squint Effect](https://mlsanity.com/paper/2403.12813/) (2024)
- [Deep Learning Empowered Type-II Codebook: New Paradigm for Enhancing CSI Feedback](https://mlsanity.com/paper/2305.08081/) (2023)
- [Joint Channel Estimation and Feedback with Masked Token Transformers in Massive MIMO Systems](https://mlsanity.com/paper/2306.06125/) (2023)
- [Improving the Performance of R17 Type-II Codebook with Deep Learning](https://mlsanity.com/paper/2310.05962/) (2023)
- [Hierarchical ML Codebook Design for Extreme MIMO Beam Management](https://mlsanity.com/paper/2312.02178/) (2023)
- [A Low-Overhead Incorporation-Extrapolation based Few-Shot CSI Feedback Framework for Massive MIMO Systems](https://mlsanity.com/paper/2312.04062/) (2023)
- [Neural Codebook Design for Network Beam Management](https://mlsanity.com/paper/2403.03053/) (2024)
- [Learnware for CSI Feedback: Scene-specific Small Models Can Do Big](https://mlsanity.com/paper/2608.17760/) (2026)

## Abstract (arXiv)

Massive multiple-input multiple-output can obtain more performance gain by exploiting the downlink channel state information (CSI) at the base station (BS). Therefore, studying CSI feedback with limited communication resources in frequency-division duplexing systems is of great importance. Recently, deep learning (DL)-based CSI feedback has shown considerable potential. However, the existing DL-based explicit feedback schemes are difficult to deploy because current fifth-generation mobile communication protocols and systems are designed based on an implicit feedback mechanism. In this paper, we propose a DL-based implicit feedback architecture to inherit the low-overhead characteristic, which uses neural networks (NNs) to replace the precoding matrix indicator (PMI) encoding and decoding modules. By using environment information, the NNs can achieve a more refined mapping between the precoding matrix and the PMI compared with codebooks. The correlation between subbands is also used to further improve the feedback performance. Simulation results show that, for a single resource block (RB), the proposed architecture can save 25.0% and 40.0% of overhead compared with Type I codebook under two antenna configurations, respectively. For a wideband system with 52 RBs, overhead can be saved by 30.7% and 48.0% compared with Type II codebook when ignoring and considering extracting subband correlation, respectively.