Paper ID: 2403.18810

LightningNet: Distributed Graph-based Cellular Network Performance Forecasting for the Edge

Konstantinos Zacharopoulos, Georgios Koutroumpas, Ioannis Arapakis, Konstantinos Georgopoulos, Javad Khangosstar, Sotiris Ioannidis

The cellular network plays a pivotal role in providing Internet access, since it is the only global-scale infrastructure with ubiquitous mobility support. To manage and maintain large-scale networks, mobile network operators require timely information, or even accurate performance forecasts. In this paper, we propose LightningNet, a lightweight and distributed graph-based framework for forecasting cellular network performance, which can capture spatio-temporal dependencies that arise in the network traffic. LightningNet achieves a steady performance increase over state-of-the-art forecasting techniques, while maintaining a similar resource usage profile. Our architecture ideology also excels in the respect that it is specifically designed to support IoT and edge devices, giving us an even greater step ahead of the current state-of-the-art, as indicated by our performance experiments with NVIDIA Jetson.

Submitted: Feb 8, 2024