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

## TL;DR

This paper presents a self-tuning spectral clustering method for automatically designing tracking areas in 5G ultra-dense networks, reducing signaling overhead and optimizing network resource usage.

## Contribution

It introduces a novel kernel function based on network statistics and applies a self-tuning spectral clustering algorithm for adaptive TA design in 5G UDNs.

## Key findings

- Significant reduction in tracking area updates
- Decreased average paging requests per TA
- Effective automatic TA boundary determination

## 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.