ML-Based Real-Time Downlink Performance Prediction in Standalone 5G NR Using Smartphones
arXiv 2604.09632•7d744a6573eca21b08cbd92e96b0f6be9e3d20e13cad17ae9ea30365e9faa2d5
5GBLERCOTS devicesCayley graphsCoMPHHFLLLM agentsNetAgentBenchO-RANR2E-VIDagentic network managementbenchmarkingdecision-theoretic safetyedge computingfederated learningfinite-state-machinemachine learningmobile measurementsmulti-agent systemspersona-driven agentsreinforcement learning (RL) optimization of graphssrsRANthroughput predictiontopology optimizationvideo inference
Paper metadata
- arXiv ID
- 2604.09632
- 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
- 7d744a6573eca21b08cbd92e96b0f6be9e3d20e13cad17ae9ea30365e9faa2d5
- Enrichment time
- 2026-04-14T07:23:53Z
- AI-assisted enrichment
- Yes
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