| dc.description.abstract | Indoor cellular networks (ICNs) are anticipated to become a princi-pal component of 5G and beyond systems. ICNs aim at extendingnetwork coverage and enhancing users’ quality of service and ex-perience, consequently producing a substantial volume of trafficin the coming years. Despite the increasing importance that ICNswill have in cellular deployments, there is nowadays little under-standing of the type of traffic demands that they serve. Our workcontributes to closing that gap, by providing a first characterizationof the usage of mobile services across more than 4, 500 cellular an-tennas deployed at over 1, 000 indoor locations in a whole country.Our analysis reveals that ICNs inherently manifest a limited setof mobile application utilization profiles, which are not present inconventional outdoor macro base stations (BSs). We interpret theindoor traffic profiles via explainable machine learning techniques,and show how they are correlated to the indoor environment. Ourfindings show how indoor cellular demands are strongly dependenton the nature of the deployment location, which allows anticipatingthe type of demands that indoor 5G networks will have to serveand paves the way for their efficient planning and dimensioning. | es |
