ML and Smartphones Assisted Real-Time Uplink Performance Prediction in 5G Cellular System
arXiv 2604.16356•c625fb71a4d6da6ece19619daaeb40c375a4ff4ac339ba23791b24064ab568e2
5GAoIBBRBLERIABIntel Tofino2LEO satelliteMARLMPEG-DASHQUICautonomous vehiclescongestion-controldeadlines`,`network-slicing`,`pinwheel-scheduling`,`RMAB`,`AoI-sdistributed traininghandoverin-networkmachine learningprogrammable switchring-based collectivessafety-criticalschedulingsmartphonessrsRANthroughput predictionuplink performance
Paper metadata
- arXiv ID
- 2604.16356
- Version
- Not specified by this published record
- Category
- Computer Science — Networking and Internet Architecture (cs.NI)
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Evidence and limitations
- Source ID
- arxiv_cs_ni
- Record identifier
- c625fb71a4d6da6ece19619daaeb40c375a4ff4ac339ba23791b24064ab568e2
- Enrichment time
- 2026-04-21T07:23:51Z
- AI-assisted enrichment
- Yes
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