# Cellular Traffic Prediction via Deep State Space Models with Attention Mechanism

arXiv: https://arxiv.org/abs/2506.15688 · HTML: https://mlsanity.com/paper/2506.15688/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 74 references that are in the map.
- First posted on arXiv 26 May 2025.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks](https://mlsanity.com/paper/1703.07015/) (2017)
- [A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction](https://mlsanity.com/paper/1704.02971/) (2017)
- [Deep Learning for Spatio-Temporal Data Mining: A Survey](https://mlsanity.com/paper/1906.04928/) (2019)
- [KalmanNet: Neural Network Aided Kalman Filtering for Partially Known Dynamics](https://mlsanity.com/paper/2107.10043/) (2021)
- [Recurrent Kalman Networks: Factorized Inference in High-Dimensional Deep Feature Spaces](https://mlsanity.com/paper/1905.07357/) (2019)
- [Long Short-Term Memory Kalman Filters:Recurrent Neural Estimators for Pose Regularization](https://mlsanity.com/paper/1708.01885/) (2017)
- [Graph-based Deep Learning for Communication Networks: A Survey](https://mlsanity.com/paper/2106.02533/) (2021)
- [Active Learning for Wireless IoT Intrusion Detection](https://mlsanity.com/paper/1808.01412/) (2018)
- [Towards Better Forecasting by Fusing Near and Distant Future Visions](https://mlsanity.com/paper/1912.05122/) (2019)
- [DynaNet: Neural Kalman Dynamical Model for Motion Estimation and Prediction](https://mlsanity.com/paper/1908.03918/) (2019)
- [From Twitter to Traffic Predictor: Next-Day Morning Traffic Prediction Using Social Media Data](https://mlsanity.com/paper/2009.13794/) (2020)
- [Entry-Flipped Transformer for Inference and Prediction of Participant Behavior](https://mlsanity.com/paper/2207.06235/) (2022)
- [Interpretable Deep Feature Propagation for Early Action Recognition](https://mlsanity.com/paper/2107.05122/) (2021)
- [DeepAuto: A Hierarchical Deep Learning Framework for Real-Time Prediction in Cellular Networks](https://mlsanity.com/paper/2001.01553/) (2019)

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

- [UrbanMind: Towards Urban General Intelligence via Tool-Enhanced Retrieval-Augmented Generation and Multilevel Optimization](https://mlsanity.com/paper/2507.04706/) (2025)
- [Multimodal Spatiotemporal-Frequency Fusion with Peak Enhancement for Cellular Traffic Forecasting](https://mlsanity.com/paper/2607.07016/) (2026)
- [Scene-Conditioned Relation Routing for urban cellular activity forecasting](https://mlsanity.com/paper/2609.20209/) (2026)

## Abstract (arXiv)

Cellular traffic prediction is of great importance for operators to manage network resources and make decisions. Traffic is highly dynamic and influenced by many exogenous factors, which would lead to the degradation of traffic prediction accuracy. This paper proposes an end-to-end framework with two variants to explicitly characterize the spatiotemporal patterns of cellular traffic among neighboring cells. It uses convolutional neural networks with an attention mechanism to capture the spatial dynamics and Kalman filter for temporal modelling. Besides, we can fully exploit the auxiliary information such as social activities to improve prediction performance. We conduct extensive experiments on three real-world datasets. The results show that our proposed models outperform the state-of-the-art machine learning techniques in terms of prediction accuracy.