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arXiv · 1902.01342

Self-Tuning Spectral Clustering for Adaptive Tracking Areas Design in 5G Ultra-Dense Networks

Brahim Aamer·Hatim Chergui·Nouamane Chergui·Kamel Tourki·Mustapha Benjillali·Christos Verikoukis·Mérouane Debbah
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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 $Q$-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.

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Brahim Aamer, Hatim Chergui, Nouamane Chergui, Kamel Tourki, Mustapha Benjillali, Christos Verikoukis, Mérouane Debbah. 2019-02-04. Self-Tuning Spectral Clustering for Adaptive Tracking Areas Design in 5G Ultra-Dense Networks. https://arxiv.org/abs/1902.01342

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