On leveraging machine and deep learning for Throughput Prediction in cellular networks: Design, performance, and challenges

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dc.contributor.authorRaca, Darijo
dc.contributor.authorZahran, Ahmed H.
dc.contributor.authorSreenan, Cormac J.
dc.contributor.authorSinha, Rakesh K.
dc.contributor.authorHalepovic, Emir
dc.contributor.authorJana, Rittwik
dc.contributor.authorGopalakrishnan, Vijay
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2020-04-27T11:45:24Z
dc.date.available2020-04-27T11:45:24Z
dc.date.issued2020-03-18
dc.date.updated2020-04-27T11:30:30Z
dc.description.abstractThe highly dynamic wireless communication environment poses a challenge for many applications (e.g., adaptive multimedia streaming services). Providing accurate TP can significantly improve performance of these applications. The scheduling algorithms in cellular networks consider various PHY metrics, (e.g., CQI) and throughput history when assigning resources for each user. This article explains how AI can be leveraged for accurate TP in cellular networks using PHY and application layer metrics. We present key architectural components and implementation options, illustrating their advantages and limitations. We also highlight key design choices and investigate their impact on prediction accuracy using real data. We believe this is the first study that examines the impact of integrating network-level data and applying a deep learning technique (on PHY and application data) for TP in cellular systems. Using video streaming as a use case, we illustrate how accurate TP improves the end user's QoE. Furthermore, we identify open questions and research challenges in the area of AI-driven TP. Finally, we report on lessons learned and provide conclusions that we believe will be useful to network practitioners seeking to apply AI.en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationRaca, D., Zahran, A. H., Sreenan, C. J., Sinha, R. K., Halepovic, E., Jana, R. and Gopalakrishnan, V. (2020) 'On leveraging machine and deep learning for Throughput Prediction in cellular networks: Design, performance, and challenges', IEEE Communications Magazine, 58(3), pp. 11-17. doi: 10.1109/MCOM.001.1900394en
dc.identifier.doi10.1109/MCOM.001.1900394en
dc.identifier.endpage17en
dc.identifier.issn0163-6804
dc.identifier.issued3en
dc.identifier.journaltitleIEEE Communications Magazineen
dc.identifier.startpage11en
dc.identifier.urihttps://hdl.handle.net/10468/9865
dc.identifier.volume58en
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Investigator Programme/13/IA/1892/IE/An Internet Infrastructure for Video Streaming Optimisation (iVID)/en
dc.rights© 2020, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en
dc.subjectAdaptive multimedia streaming servicesen
dc.subjectAIen
dc.subjectTPen
dc.subjectNetwork-level dataen
dc.subjectDeep learning techniqueen
dc.subjectAI-driven TPen
dc.subjectThroughput Predictionen
dc.titleOn leveraging machine and deep learning for Throughput Prediction in cellular networks: Design, performance, and challengesen
dc.typeArticle (peer-reviewed)en
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