mlsanityData to 14 Oct 2026

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

arXiv 2403.18810 ↗First posted 8 Feb 2024cs.NI cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 16 of its 64 references that are in the map. First posted on arXiv 8 Feb 2024.

Builds on

16 of 64 references.

References · 16

  1. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,057
  2. 2014Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modelingcited by 2,367
  3. 2018Relational inductive biases, deep learning, and graph networkscited by 1,360
  4. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,136
  5. 2017DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networkscited by 1,013
  6. 2017Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networkscited by 879
  7. 2018T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Predictioncited by 401
  8. 2018BRITS: Bidirectional Recurrent Imputation for Time Seriescited by 290
  9. 2018Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecastingcited by 146
  10. 2017Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Predictioncited by 67
  11. 2018Relational inductive bias for physical construction in humans and machinescited by 44
  12. 2017Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecastingcited by 41
4 more
  1. 2019Joint Modeling of Local and Global Temporal Dynamics for Multivariate Time Series Forecasting with Missing Valuescited by 37
  2. 2017Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networkscited by 35
  3. 2020Knowledge Adaption for Demand Prediction based on Multi-task Memory Neural Networkcited by 6
  4. 2017Hot or not? Forecasting cellular network hot spots using sector performance indicatorscited by 2

Built on by

Not cited yet.