# Deep Learning based Downlink Channel Prediction for FDD Massive MIMO System

arXiv: https://arxiv.org/abs/1908.03360 · HTML: https://mlsanity.com/paper/1908.03360/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Aug 2019.

## Built on by (most cited first, 13 of 13)

- [RLOps: Development Life-cycle of Reinforcement Learning Aided Open RAN](https://mlsanity.com/paper/2111.06978/) (2021)
- [Deep Learning-based Implicit CSI Feedback in Massive MIMO](https://mlsanity.com/paper/2105.10100/) (2021)
- [CAnet: Uplink-aided Downlink Channel Acquisition in FDD Massive MIMO using Deep Learning](https://mlsanity.com/paper/2101.04377/) (2021)
- [Binarized Aggregated Network with Quantization: Flexible Deep Learning Deployment for CSI Feedback in Massive MIMO System](https://mlsanity.com/paper/2105.00354/) (2021)
- [Spectral Temporal Graph Neural Network for massive MIMO CSI Prediction](https://mlsanity.com/paper/2312.02159/) (2023)
- [FedsLLM: Federated Split Learning for Large Language Models over Communication Networks](https://mlsanity.com/paper/2407.09250/) (2024)
- [A Variational Auto-Encoder Enabled Multi-Band Channel Prediction Scheme for Indoor Localization](https://mlsanity.com/paper/2309.12200/) (2023)
- [Aggregated Network for Massive MIMO CSI Feedback](https://mlsanity.com/paper/2101.06618/) (2021)
- [Deep Generative Models for Downlink Channel Estimation in FDD Massive MIMO Systems](https://mlsanity.com/paper/2203.04935/) (2022)
- [Channel-Feedback-Free Transmission for Downlink FD-RAN: A Radio Map based Complex-valued Precoding Network Approach](https://mlsanity.com/paper/2312.02184/) (2023)
- [Downlink Channel Covariance Matrix Estimation via Representation Learning with Graph Regularization](https://mlsanity.com/paper/2407.18865/) (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)
- [CSI-4CAST: A Hybrid Deep Learning Model for CSI Prediction with Comprehensive Robustness and Generalization Testing](https://mlsanity.com/paper/2510.12996/) (2025)

## Abstract (arXiv)

In a frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) system, the acquisition of downlink channel state information (CSI) at base station (BS) is a very challenging task due to the overwhelming overheads required for downlink training and uplink feedback. In this paper, we reveal a deterministic uplink-to-downlink mapping function when the position-to-channel mapping is bijective. Motivated by the universal approximation theorem, we then propose a sparse complex-valued neural network (SCNet) to approximate the uplink-to-downlink mapping function. Different from general deep networks that operate in the real domain, the SCNet is constructed in the complex domain and is able to learn the complex-valued mapping function by off-line training. After training, the SCNet is used to directly predict the downlink CSI based on the estimated uplink CSI without the need of either downlink training or uplink feedback. Numerical results show that the SCNet achieves better performance than general deep networks in terms of prediction accuracy and exhibits remarkable robustness over complicated wireless channels, demonstrating its great potential for practical deployments.