Cellular Traffic Prediction via Deep State Space Models with Attention Mechanism

TL;DR

This paper introduces a deep state space model with attention for cellular traffic prediction, effectively capturing spatiotemporal patterns and leveraging auxiliary data to improve accuracy.

Contribution

It proposes a novel end-to-end framework combining CNNs, attention, and Kalman filters for enhanced cellular traffic prediction, incorporating auxiliary information.

Findings

Outperforms state-of-the-art machine learning methods in accuracy

Effectively models spatiotemporal traffic patterns

Utilizes auxiliary data to boost prediction performance

Abstract

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.