AI-Powered Real-Time Channel Awareness and 5G NR Radio Access Network Scheduling Optimization

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dc.contributor.authorWang, Yingen
dc.contributor.authorGorski, Adamen
dc.contributor.authorDaSilva, Luiz A.en
dc.date.accessioned2022-01-06T15:32:55Zen
dc.date.available2022-01-06T15:32:55Zen
dc.date.issued2021-04-19en
dc.date.updated2022-01-06T15:32:53Zen
dc.description.abstractAs with any other wireless technology, 5G is not immune to jamming. To achieve consistent performance, network resource scheduling must be optimized in a way that reacts to jamming in the NR channel environment. This paper presents a cognitive system for real-time Channel Awareness and Radio Access Network (RAN) Scheduling (CARS) optimization based on multi-dimensional temporal machine learning models. Our system automatically detects and classifies jamming in the channel environment and optimizes scheduling based on classification results and collected link parameters. Based on over-the-air (OTA) experiments, detection and classification time is less than 0.8 seconds, which enables real-time optimization. The system is evaluated and verified for OTA experimentation through integration to our end-to-end NR system. An Automated Jamming Module (AJM) is designed and implemented. Connecting the AJM to our NR system enables a comprehensive evaluation environment for our Jamming Detection and Classification Model (JDCM) and Modulation and Coding Scheme optimization model. The improvement in connection resiliency against Control Resource Set jamming is proof of the CARS concept for real-time channel awareness and scheduling optimization. Depending on channel conditions, CARS achieves a 30% or higher improvement in NR system throughput.en
dc.description.versionAccepted versionen
dc.format.extentPages 1-7en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1109/drcn51631.2021.9477326en
dc.identifier.urihttp://hdl.handle.net/10919/107422en
dc.language.isoenen
dc.publisherIEEEen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.titleAI-Powered Real-Time Channel Awareness and 5G NR Radio Access Network Scheduling Optimizationen
dc.title.serial2021 17th International Conference on the Design of Reliable Communication Networks (DRCN)en
dc.typeConference proceedingen
dc.type.dcmitypeTexten
pubs.finish-date2021-04-22en
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/University Research Institutesen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.start-date2021-04-19en

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