# Spectral Temporal Graph Neural Network for massive MIMO CSI Prediction

arXiv: https://arxiv.org/abs/2312.02159 · HTML: https://mlsanity.com/paper/2312.02159/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 18 references that are in the map.
- First posted on arXiv 10 Sep 2023.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Time Series Forecasting With Deep Learning: A Survey](https://mlsanity.com/paper/2004.13408/) (2020)
- [Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting](https://mlsanity.com/paper/2103.07719/) (2021)
- [Overview of Deep Learning-based CSI Feedback in Massive MIMO Systems](https://mlsanity.com/paper/2206.14383/) (2022)
- [Deep Learning based Downlink Channel Prediction for FDD Massive MIMO System](https://mlsanity.com/paper/1908.03360/) (2019)
- [A Spatially Separable Attention Mechanism for massive MIMO CSI Feedback](https://mlsanity.com/paper/2208.03369/) (2022)
- [Graph Neural Networks-Based User Pairing in Wireless Communication Systems](https://mlsanity.com/paper/2306.00717/) (2023)

## Built on by (most cited first, 3 of 3)

- [CSI Prediction Using Diffusion Models](https://mlsanity.com/paper/2510.11214/) (2025)
- [CSI-4CAST: A Hybrid Deep Learning Model for CSI Prediction with Comprehensive Robustness and Generalization Testing](https://mlsanity.com/paper/2510.12996/) (2025)
- [ChannelKAN: Multi-Scale Dual-Domain Channel Prediction via Hybrid CNN-KAN Architecture](https://mlsanity.com/paper/2605.12553/) (2026)

## Abstract (arXiv)

In the realm of 5G communication systems, the accuracy of Channel State Information (CSI) prediction is vital for optimizing performance. This letter introduces a pioneering approach: the Spectral-Temporal Graph Neural Network (STEM GNN), which fuses spatial relationships and temporal dynamics of the wireless channel using the Graph Fourier Transform. We compare the STEM GNN approach with conventional Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) models for CSI prediction. Our findings reveal a significant enhancement in overall communication system performance through STEM GNNs. For instance, in one scenario, STEM GNN achieves a sum rate of 5.009 bps/Hz which is 11.9% higher than that of LSTM and 35% higher than that of RNN. The spectral-temporal analysis capabilities of STEM GNNs capture intricate patterns often overlooked by traditional models, offering improvements in beamforming, interference mitigation, and ultra-reliable low-latency communication (URLLC).