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dc.contributor.authorFezeu, Rostand A. K.
dc.contributor.authorFiandrino, Claudio 
dc.contributor.authorRamadan, Eman
dc.contributor.authorCarpenter, Jason
dc.contributor.authorCoelho de Freitas, Lilian
dc.contributor.authorBilal, Faaiq
dc.contributor.authorYe, Wei
dc.contributor.authorWidmer, Joerg 
dc.contributor.authorQian, Feng
dc.contributor.authorZhang, Zhi-Li 
dc.date.accessioned2024-08-14T09:05:01Z
dc.date.available2024-08-14T09:05:01Z
dc.date.issued2024-08
dc.identifier.urihttps://hdl.handle.net/20.500.12761/1840
dc.description.abstract5G in mid-bands has become the dominant deployment of choice in the world. We present - to the best of our knowledge - the first comprehensive and comparative cross-country measurement study of commercial mid-band 5G deployments in Europe and the U.S., filling a gap in the existing 5G measurement studies. We unveil the key 5G mid-band channels and configuration parameters used by various operators in these countries, and identify the major factors that impact the observed 5G performance both from the network (physical layer) perspective as well as the application perspective. We characterize and compare 5G mid-band throughput and latency performance by dissecting the 5G configurations, lower-layer parameters as well as deployment settings. By cross-correlating 5G parameters with the application decision process, we demonstrate how 5G parameters affect application QoE metrics and suggest a simple approach for QoE enhancement. Our study sheds light on how to better configure and optimize 5G mid-band networks, and provides guidance to users and application developers on operator choices and application QoE tuning. We released the datasets and artifacts at https://github.com/SIGCOMM24-5GinMidBands/artifacts.es
dc.description.sponsorshipMinisterio de Ciencia e Innovaciónes
dc.description.sponsorshipMinisterio de Universidadeses
dc.language.isoenges
dc.titleUnveiling the 5G Mid-Band Landscape: From Network Deployment to Performance and Application QoEes
dc.typeconference objectes
dc.conference.date4-8 August 2024es
dc.conference.placeSidney, Australiaes
dc.conference.titleACM Special Interest Group on Management of Data Conference *
dc.event.typeconferencees
dc.pres.typepaperes
dc.rights.accessRightsopen accesses
dc.acronymSIGMOD*
dc.rankA**
dc.relation.projectNamebRAIN (Explainable and robust AI for integration in next generation networked systems)es
dc.relation.projectNameRISC-6G (Reconfigurable Intelligent Surfaces and Low-power Technologies for Communication and Sensing in 6G Mobile Networks)es
dc.relation.projectNameMAP-6G (Machine Learning-based Privacy Preserving Analytics for 6G Mobile Networks)es
dc.relation.projectNameRamon y Cajal RYC2022-036375-I Claudio Fiandrinoes
dc.description.refereedTRUEes
dc.description.statuspubes


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