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.

Highlights

  • A KEY component in wireless networks is user location management

  • The results clearly showed the ability of dynamic tracking areas lists (TALs) in reducing the signaling overhead and maintaining a good performance due to reconfiguration compared to the conventional tracking areas (TAs) design

  • These features stem from automatic neighbor relation (ANR) statistics retrieved from the self-organizing networks (SON) platform of a large 4G live network

Read more

Summary

INTRODUCTION

A KEY component in wireless networks is user location management Such a function is achieved using the concept of tracking area (TA); to location area (LA) in GSM and routing area (RA) in GPRS. When a user moves into a new TA, an update message is sent to the MME. This causes a signaling overhead, referred to as the update overhead. In order to place a call to a user, MME broadcasts a paging message in all cells of the TA in which the user is currently registered. An optimal TA design tends to group new radio node Bs (gNBs) having large numbers of users roaming between them, e.g., gNBs along a road with much traffic, into the same TA. The dynamic user behavior and traffic patterns in urban environments make that TAs, initially optimized for certain user statistics (or forecasts), become inaccurate and urge to implement machine learningdriven adaptive TA design algorithms as part of the so-called self-organizing networks (SON) framework [2]

Related Work
Contributions
Live Network Dataset
Similarity Matrix
Self-Tuning Spectral Clustering
PERFORMANCE ASSESSMENT
Number of Clusters
TAU and Paging Performance Gains
STSC Quality
Silhouette Score
Findings
CONCLUSION
Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call