Back to the future: Throughput prediction for cellular networks using radio KPIs

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dc.contributor.author Raca, Darijo
dc.contributor.author Zahran, Ahmed H.
dc.contributor.author Sreenan, Cormac J.
dc.contributor.author Sinha, Rakesh K.
dc.contributor.author Halepovic, Emir
dc.contributor.author Jana, Rittwik
dc.contributor.author Gopalakrishnan, Vijay
dc.date.accessioned 2017-10-27T08:54:35Z
dc.date.available 2017-10-27T08:54:35Z
dc.date.issued 2017-10-16
dc.identifier.citation Raca, D., Zahran, A. H., Sreenan, C. J., Sinha, R. K., Halepovic, E., Jana, R. and Gopalakrishnan, V. (2017) 'Back to the Future: Throughput Prediction For Cellular Networks using Radio KPIs', HotWireless '17 Proceedings of the 4th ACM Workshop on Hot Topics in Wireless, Snowbird, Utah, USA, 16 October, New York: ACM, pp. 37-41. doi:10.1145/3127882.3127892 en
dc.identifier.startpage 37 en
dc.identifier.endpage 41 en
dc.identifier.isbn 978-1-4503-5140-9
dc.identifier.uri http://hdl.handle.net/10468/4934
dc.identifier.doi 10.1145/3127882.3127892
dc.description.abstract The availability of reliable predictions for cellular throughput would offer a fundamental change in the way applications are designed and operated. Numerous cellular applications, including video streaming and VoIP, embed logic that attempts to estimate achievable throughput and adapt their behaviour accordingly. We believe that providing applications with reliable predictions several seconds into the future would enable profoundly better adaptation decisions and dramatically benefit demanding applications like mobile virtual and augmented reality. The question we pose and seek to address is whether such reliable predictions are possible. We conduct a preliminary study of throughput prediction in a cellular environment using statistical machine learning techniques. An accurate prediction can be very challenging in large scale cellular environments because they are characterized by highly fluctuating channel conditions. Using simulations and real-world experiments, we study how prediction error varies as a function of prediction horizon, and granularity of available data. In particular, our simulation experiments show that the prediction error for mobile devices can be reduced significantly by combining measurements from the network with measurements from the end device. Our results indicate that it is possible to accurately predict achievable throughput up to 8 sec in the future where 50th percentile of all errors are less than 15% for mobile and 2% for static devices. en
dc.description.uri https://dl.acm.org/citation.cfm?id=3127882&picked=prox&CFID=999211294&CFTOKEN=83163054 en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Association for Computing Machinery (ACM) en
dc.rights © 2017 Association for Computing Machinery. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in HotWireless '17 Proceedings of the 4th ACM Workshop on Hot Topics in Wireless, http://dx.doi.org/10.1145/3127882.3127892 en
dc.subject Celluar network en
dc.subject Throughput guidance en
dc.subject Machine learning en
dc.subject Networks en
dc.subject Network performance evaluation en
dc.subject Network types en
dc.subject Mobile networks en
dc.subject Computing methodologies en
dc.title Back to the future: Throughput prediction for cellular networks using radio KPIs en
dc.type Article (peer-reviewed) en
dc.type Conference item en
dc.internal.authorcontactother Cormac J. Sreenan, Computer Science, University College Cork, Cork, Ireland. +353-21-490-3000 Email: cjs@cs.ucc.ie en
dc.internal.availability Full text available en
dc.description.version Accepted Version en
dc.contributor.funder Science Foundation Ireland en
dc.description.status Peer reviewed en
dc.identifier.journaltitle HotWireless '17 Proceedings of the 4th ACM Workshop on Hot Topics in Wireless en
dc.internal.copyrightchecked !!CORA!! en
dc.internal.conferencelocation Snowbird, UT, USA en
dc.internal.IRISemailaddress cjs@cs.ucc.ie en
dc.relation.project info:eu-repo/grantAgreement/SFI/SFI Investigator Programme/13/IA/1892/IE/An Internet Infrastructure for Video Streaming Optimisation (iVID)/ en


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