ML and Smartphones Assisted Real-Time Uplink Performance Prediction in 5G Cellular System
2026-04-21T07:23:51Z•c625fb71a4d6da6ece19619daaeb40c375a4ff4ac339ba23791b24064ab568e2
5GAoIBBRBLERIABIntel Tofino2LEO satelliteMARLMPEG-DASHQUICautonomous vehiclescongestion-controldeadlines`,`network-slicing`,`pinwheel-scheduling`,`RMAB`,`AoI-sdistributed traininghandoverin-networkmachine learningprogrammable switchring-based collectivessafety-criticalschedulingsmartphonessrsRANthroughput predictionuplink performance
What happened
This batch of CS/Networking papers (arXiv) covers applied research on 5G/LEO-satellite networks, in-network scheduling for distributed AI, age-of-information (AoI) and deadline-aware scheduling, wireless/optical resource configuration, and decentralized trust/IDS enhancements at the IoT edge. Key contributions: (1) ML + COTS smartphones predict 5G uplink throughput/BLER in a private srsRAN testbed (useful for network monitoring and optimization, with potential information‑leakage/traffic-analysis implications); (2) end-to-end MPEG-DASH streaming over LEO-based 5G IAB showing QUIC+BBR tradeoffs
Why it matters
A reviewed impact interpretation has not been published for this record.
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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