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Mobile Information Systems
Volume 2017, Article ID 2752364, 10 pages
https://doi.org/10.1155/2017/2752364
Research Article

A Trace Data-Based Approach for an Accurate Estimation of Precise Utilization Maps in LTE

1Departamento de Ingeniería de Comunicaciones, E.T.S.I. Telecomunicación, Universidad de Málaga, Bulevar Louis Pasteur, S/N, 29010 Malaga, Spain
2Ericsson, C/Vía de los Poblados 13, 28033 Madrid, Spain

Correspondence should be addressed to Almudena Sánchez; se.amu.ci@anedumla

Received 3 February 2017; Accepted 3 April 2017; Published 8 May 2017

Academic Editor: Donghoon Shin

Copyright © 2017 Almudena Sánchez et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

For network planning and optimization purposes, mobile operators make use of Key Performance Indicators (KPIs), computed from Performance Measurements (PMs), to determine whether network performance needs to be improved. In current networks, PMs, and therefore KPIs, suffer from lack of precision due to an insufficient temporal and/or spatial granularity. In this work, an automatic method, based on data traces, is proposed to improve the accuracy of radio network utilization measurements collected in a Long-Term Evolution (LTE) network. The method’s output is an accurate estimate of the spatial and temporal distribution for the cell utilization ratio that can be extended to other indicators. The method can be used to improve automatic network planning and optimization algorithms in a centralized Self-Organizing Network (SON) entity, since potential issues can be more precisely detected and located inside a cell thanks to temporal and spatial precision. The proposed method is tested with real connection traces gathered in a large geographical area of a live LTE network and considers overload problems due to trace file size limitations, which is a key consideration when analysing a large network. Results show how these distributions provide a very detailed information of network utilization, compared to cell based statistics.