基于信息论的蜂窝网络使用数据聚类,用于识别代表性城市区域

IF 7.5 2区 计算机科学 Q1 TELECOMMUNICATIONS
Mihaela I. Chidean , Luis Ignacio Jiménez Gil , Javier Carmona-Murillo , David Cortés-Polo
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引用次数: 0

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Information theory based clustering of cellular network usage data for the identification of representative urban areas
The exponential growth of the number of network devices in recent years not only entails the need for automation of management tasks, but also leads to the increase of available network data and metadata. 5G and beyond standards already cover those requirements and also include the need to define and use machine learning techniques to take advantage of the data acquired, especially using geolocated Call Detail Record (CDR) data sets. However, this scenario requires novel cellular network analysis methodologies to exploit all these available data, especially for the network usage pattern in order to ease the management tasks. In this work, a novel method based on information theory metrics like the Kullback-Leibler divergence and data classification algorithms is proposed to identify representative urban areas in terms of the network usage pattern. Methodology validation is performed via computer analysis using the Open Big Data CDR data set in the Milan area for different scenarios. Obtained results validate the proposed methodology and also reveal its adaptability in terms of specific scenario characteristics. Network usage patterns are calculated for each representative area, paving the path to several future research lines in network management, such as network usage prediction based on this methodology and using the comportment time series.
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来源期刊
Digital Communications and Networks
Digital Communications and Networks Computer Science-Hardware and Architecture
CiteScore
12.80
自引率
5.10%
发文量
915
审稿时长
30 weeks
期刊介绍: Digital Communications and Networks is a prestigious journal that emphasizes on communication systems and networks. We publish only top-notch original articles and authoritative reviews, which undergo rigorous peer-review. We are proud to announce that all our articles are fully Open Access and can be accessed on ScienceDirect. Our journal is recognized and indexed by eminent databases such as the Science Citation Index Expanded (SCIE) and Scopus. In addition to regular articles, we may also consider exceptional conference papers that have been significantly expanded. Furthermore, we periodically release special issues that focus on specific aspects of the field. In conclusion, Digital Communications and Networks is a leading journal that guarantees exceptional quality and accessibility for researchers and scholars in the field of communication systems and networks.
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