Machine Learning-Driven Content Popularity Prediction and Cache Optimization in D2D Clustered Networks
arXiv 2606.26119•4fc97e1150fdc0f69c630d901c09ad3c6f41ac973d40388f9ecf233585f984ef
5G NR-V2X6GD2D cachingLLMMARLRISSAGINV2Xautonomous sysadmindevice-to-deviceedge AIedge computingfairnessfederated learningnetwork resilienceprivacyreconfigurable intelligent surfaceroutingsafetysatellite networks
Paper metadata
- arXiv ID
- 2606.26119
- 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
- 4fc97e1150fdc0f69c630d901c09ad3c6f41ac973d40388f9ecf233585f984ef
- Enrichment time
- 2026-06-26T07:23:52Z
- AI-assisted enrichment
- Yes
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