# Deep Transfer Learning Based Downlink Channel Prediction for FDD Massive MIMO Systems

arXiv: https://arxiv.org/abs/1912.12265 · HTML: https://mlsanity.com/paper/1912.12265/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Dec 2019.

## Built on by (most cited first, 19 of 19)

- [Overview of Deep Learning-based CSI Feedback in Massive MIMO Systems](https://mlsanity.com/paper/2206.14383/) (2022)
- [Transfer Learning for Future Wireless Networks: A Comprehensive Survey](https://mlsanity.com/paper/2102.07572/) (2021)
- [Holistic Network Virtualization and Pervasive Network Intelligence for 6G](https://mlsanity.com/paper/2301.00519/) (2023)
- [Deep Learning-based Implicit CSI Feedback in Massive MIMO](https://mlsanity.com/paper/2105.10100/) (2021)
- [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)
- [DeepTx: Deep Learning Beamforming with Channel Prediction](https://mlsanity.com/paper/2202.07998/) (2022)
- [Learning at the Speed of Wireless: Online Real-Time Learning for AI-Enabled MIMO in NextG](https://mlsanity.com/paper/2403.02651/) (2024)
- [Generative Diffusion Receivers: Achieving Pilot-Efficient MIMO-OFDM Communications](https://mlsanity.com/paper/2506.18419/) (2025)
- [Predicting Multi-Antenna Frequency-Selective Channels via Meta-Learned Linear Filters based on Long-Short Term Channel Decomposition](https://mlsanity.com/paper/2203.12715/) (2022)
- [Enabling Deep Learning-based Physical-layer Secret Key Generation for FDD-OFDM Systems in Multi-Environments](https://mlsanity.com/paper/2211.03065/) (2022)
- [MMSense: Adapting Vision-based Foundation Model for Multi-task Multi-modal Wireless Sensing](https://mlsanity.com/paper/2511.12305/) (2025)
- [Deep Learning Based Antenna-time Domain Channel Extrapolation for Hybrid mmWave Massive MIMO](https://mlsanity.com/paper/2108.03941/) (2021)
- [Deep Learning of Transferable MIMO Channel Modes for 6G V2X Communications](https://mlsanity.com/paper/2108.13831/) (2021)
- [Multi-task Deep Neural Networks for Massive MIMO CSI Feedback](https://mlsanity.com/paper/2204.12442/) (2022)
- [Science-Informed Design of Deep Learning With Applications to Wireless Systems: A Tutorial](https://mlsanity.com/paper/2407.07742/) (2024)
- [DRL-based Dolph-Tschebyscheff Beamforming in Downlink Transmission for Mobile Users](https://mlsanity.com/paper/2502.01278/) (2025)
- [Standards-Compliant DM-RS Allocation via Temporal Channel Prediction for Massive MIMO Systems](https://mlsanity.com/paper/2507.11064/) (2025)
- [SCA-LLM: Spectral-Attentive LLM-Based Wireless World Modeling for Agentic Communications](https://mlsanity.com/paper/2509.08139/) (2025)
- [Resilience Characterization of AI-Native Wireless Receivers via Persistent Homology](https://mlsanity.com/paper/2605.22886/) (2026)

## Abstract (arXiv)

Artificial intelligence (AI) based downlink channel state information (CSI) prediction for frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems has attracted growing attention recently. However, existing works focus on the downlink CSI prediction for the users under a given environment and is hard to adapt to users in new environment especially when labeled data is limited. To address this issue, we formulate the downlink channel prediction as a deep transfer learning (DTL) problem, where each learning task aims to predict the downlink CSI from the uplink CSI for one single environment. Specifically, we develop the direct-transfer algorithm based on the fully-connected neural network architecture, where the network is trained on the data from all previous environments in the manner of classical deep learning and is then fine-tuned for new environments. To further improve the transfer efficiency, we propose the meta-learning algorithm that trains the network by alternating inner-task and across-task updates and then adapts to a new environment with a small number of labeled data. Simulation results show that the direct-transfer algorithm achieves better performance than the deep learning algorithm, which implies that the transfer learning benefits the downlink channel prediction in new environments. Moreover, the meta-learning algorithm significantly outperforms the direct-transfer algorithm in terms of both prediction accuracy and stability, which validates its effectiveness and superiority.