MetaSTNet: Multimodal Meta-learning for Cellular Traffic Conformal Prediction

TL;DR

MetaSTNet is a deep learning model that uses multimodal meta-learning and conformal prediction to accurately forecast cellular network traffic with limited training data, improving adaptability and reliability.

Contribution

The paper introduces MetaSTNet, a novel multimodal meta-learning framework that transfers knowledge from simulation to real-world environments for quick adaptation with minimal data.

Findings

MetaSTNet achieves high prediction accuracy with limited real-world data.

The model effectively estimates calibrated prediction intervals.

Extensive experiments validate its efficiency and effectiveness.

Abstract

Network traffic prediction techniques have attracted much attention since they are valuable for network congestion control and user experience improvement. While existing prediction techniques can achieve favorable performance when there is sufficient training data, it remains a great challenge to make accurate predictions when only a small amount of training data is available. To tackle this problem, we propose a deep learning model, entitled MetaSTNet, based on a multimodal meta-learning framework. It is an end-to-end network architecture that trains the model in a simulator and transfers the meta-knowledge to a real-world environment, which can quickly adapt and obtain accurate predictions on a new task with only a small amount of real-world training data. In addition, we further employ cross conformal prediction to assess the calibrated prediction intervals. Extensive experiments have been conducted on real-world datasets to illustrate the efficiency and effectiveness of MetaSTNet.