Self-Tuning Spectral Clustering for Adaptive Tracking Areas Design in 5G Ultra-Dense Networks
Abstract
In this paper, we address the issue of automatic tracking areas (TAs) planning in fifth generation (5G) ultra-dense networks (UDNs). By invoking handover (HO) attempts and measurement reports (MRs) statistics of a 4G live network, we first introduce a new kernel function mapping HO attempts, MRs and inter-site distances (ISDs) into the so-called similarity weight. The corresponding matrix is then fed to a self-tuning spectral clustering (STSC) algorithm to automatically define the TAs number and borders. After evaluating its performance in terms of the -metric as well as the silhouette score for various kernel parameters, we show that the clustering scheme yields a significant reduction of tracking area updates and average paging requests per TA; optimizing thereby network resources.
Keywords
Cite
@article{arxiv.1902.01342,
title = {Self-Tuning Spectral Clustering for Adaptive Tracking Areas Design in 5G Ultra-Dense Networks},
author = {Brahim Aamer and Hatim Chergui and Nouamane Chergui and Kamel Tourki and Mustapha Benjillali and Christos Verikoukis and Mérouane Debbah},
journal= {arXiv preprint arXiv:1902.01342},
year = {2019}
}
Comments
IEEE WCNC'2019 (Accepted)