# CAnet: Uplink-aided Downlink Channel Acquisition in FDD Massive MIMO using Deep Learning

arXiv: https://arxiv.org/abs/2101.04377 · HTML: https://mlsanity.com/paper/2101.04377/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 46 references that are in the map.
- First posted on arXiv 12 Jan 2021.

## Builds on (most cited first, 16 of 16)

- [To prune, or not to prune: exploring the efficacy of pruning for model compression](https://mlsanity.com/paper/1710.01878/) (2017)
- [DuDoRNet: Learning a Dual-Domain Recurrent Network for Fast MRI Reconstruction with Deep T1 Prior](https://mlsanity.com/paper/2001.03799/) (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)
- [What Will 5G Be?](https://mlsanity.com/paper/1405.2957/) (2014)
- [Distributed Deep Convolutional Compression for Massive MIMO CSI Feedback](https://mlsanity.com/paper/2003.04684/) (2020)
- [A Speculative Study on 6G](https://mlsanity.com/paper/1902.06700/) (2019)
- [Deep Learning for Joint Channel Estimation and Feedback in Massive MIMO Systems](https://mlsanity.com/paper/2011.07242/) (2020)
- [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)
- [Adversarial Attack on DL-based Massive MIMO CSI Feedback](https://mlsanity.com/paper/2002.09896/) (2020)
- [Binary Neural Network Aided CSI Feedback in Massive MIMO System](https://mlsanity.com/paper/2011.02692/) (2020)
- [Joint Antenna Selection and Hybrid Beamformer Design using Unquantized and Quantized Deep Learning Networks](https://mlsanity.com/paper/1905.03107/) (2019)
- [Deep Multimodal Learning: Merging Sensory Data for Massive MIMO Channel Prediction](https://mlsanity.com/paper/2007.09366/) (2020)
- [Jointly Sparse Signal Recovery and Support Recovery via Deep Learning with Applications in MIMO-based Grant-Free Random Access](https://mlsanity.com/paper/2008.01992/) (2020)
- [Joint Design of Measurement Matrix and Sparse Support Recovery Method via Deep Auto-encoder](https://mlsanity.com/paper/1910.04330/) (2019)

## Built on by (most cited first, 6 of 6)

- [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)
- [CSI-GPT: Integrating Generative Pre-Trained Transformer with Federated-Tuning to Acquire Downlink Massive MIMO Channels](https://mlsanity.com/paper/2406.03438/) (2024)
- [Channel Modeling Aided Dataset Generation for AI-Enabled CSI Feedback: Advances, Challenges, and Solutions](https://mlsanity.com/paper/2407.00896/) (2024)
- [Physics-Inspired Deep Learning Anti-Aliasing Framework in Efficient Channel State Feedback](https://mlsanity.com/paper/2403.08133/) (2024)
- [Uplink Assisted Joint Channel Estimation and CSI Feedback: An Approach Based on Deep Joint Source-Channel Coding](https://mlsanity.com/paper/2504.10836/) (2025)

## Abstract (arXiv)

In frequency-division duplexing systems, the downlink channel state information (CSI) acquisition scheme leads to high training and feedback overheads. In this paper, we propose an uplink-aided downlink channel acquisition framework using deep learning to reduce these overheads. Unlike most existing works that focus only on channel estimation or feedback modules, to the best of our knowledge, this is the first study that considers the entire downlink CSI acquisition process, including downlink pilot design, channel estimation, and feedback. First, we propose an adaptive pilot design module by exploiting the correlation in magnitude among bidirectional channels in the angular domain to improve channel estimation. Next, to avoid the bit allocation problem during the feedback module, we concatenate the complex channel and embed the uplink channel magnitude to the channel reconstruction at the base station. Lastly, we combine the above two modules and compare two popular downlink channel acquisition frameworks. The former framework estimates and feeds back the channel at the user equipment subsequently. The user equipment in the latter one directly feeds back the received pilot signals to the base station. Our results reveal that, with the help of uplink, directly feeding back the pilot signals can save approximately 20% of feedback bits, which provides a guideline for future research.