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Characterizing Mobile Service Demands at Indoor Cellular Networks

Published: 24 October 2023 Publication History

Abstract

Indoor cellular networks (ICNs) are anticipated to become a principal component of 5G and beyond systems. ICNs aim at extending network coverage and enhancing users' quality of service and experience, consequently producing a substantial volume of traffic in the coming years. Despite the increasing importance that ICNs will have in cellular deployments, there is nowadays little understanding of the type of traffic demands that they serve. Our work contributes to closing that gap, by providing a first characterization of the usage of mobile services across more than 4, 500 cellular antennas deployed at over 1,000 indoor locations in a whole country. Our analysis reveals that ICNs inherently manifest a limited set of mobile application utilization profiles, which are not present in conventional outdoor macro base stations (BSs). We interpret the indoor traffic profiles via explainable machine learning techniques, and show how they are correlated to the indoor environment. Our findings show how indoor cellular demands are strongly dependent on the nature of the deployment location, which allows anticipating the type of demands that indoor 5G networks will have to serve and paves the way for their efficient planning and dimensioning.

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      cover image ACM Conferences
      IMC '23: Proceedings of the 2023 ACM on Internet Measurement Conference
      October 2023
      746 pages
      ISBN:9798400703829
      DOI:10.1145/3618257
      This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike International 4.0 License.

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      Published: 24 October 2023

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      Author Tags

      1. explainable machine learning
      2. indoor cellular networks
      3. mobile service demands
      4. traffic profiles

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      • European Commission

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      October 24 - 26, 2023
      Montreal QC, Canada

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