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Communication Dans Un Congrès Année : 2023

Leveraging Unsupervised Machine Learning for New Traffic Types Classification in Hybrid Satellite Terrestrial Network

Saloua Hendaoui
  • Fonction : Auteur
Nawel Zangar

Résumé

By offering real-time insights, automating network modifications, and improving network performance, unsupervised machine learning can be a useful tool for managing novel traffic types in hybrid terrestrialsatellite networks. It aids network operators in adjusting to shifting traffic patterns and addressing the needs of a dynamic and ever-changing communication landscape. We investigate the possibility of unsupervised learning based on four different learning algorithms created for identifying novel traffic with the aim of establishing a novel framework that maximizes proactive resource allocation. Based on a performance assessment that was done, the findings show that that the K-Means Clustering and Gaussian Mixture Model provide the best report between acceptable silhouette and Principal Component Analysis.
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Dates et versions

hal-04361903 , version 1 (22-12-2023)

Identifiants

  • HAL Id : hal-04361903 , version 1

Citer

Saloua Hendaoui, Nawel Zangar. Leveraging Unsupervised Machine Learning for New Traffic Types Classification in Hybrid Satellite Terrestrial Network. IEEE ISNCC'23, Oct 2023, Doha, Qatar. ⟨hal-04361903⟩

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