Multi-Agent DRL for QoS and Energy Optimization in RIS-Enabled Open-RAN Industrial 6G TN/NTN Networks

2026-06-30T07:23:49Zaa4bd7cc6c1bf1a1e3cdd4c42bbbfdf47e942a38ae96186fad98e77cfc72b4c5
5G6GCornerCaseDec-POMDPETSI-VAMHTTPIPFSLLM-assisted-testingOpen-RANRISUAV-RISV2Xbandwidth-constraintsdecentralized-learningdifferential-testingedge-computingh2ointention-sharing','uncertainty-ellipses'latencymobilitymulti-agent-DRLprotocol-bugsprotocol-testingproximity-servicesstateless-ABR

What happened

This feed aggregates recent cs.NI (networking & internet) arXiv papers covering wireless 6G/Open-RAN with UAV-mounted RIS and multi-agent DRL, 5G/edge latency breakdowns, decentralized learning under mobility/bandwidth limits, intent-driven 6G orchestration grounded with catalogs and SHACL validation, automated extremal protocol testing (CornerCase) that used LLMs to extract RFC constraints and found 42 anomalies (26 acknowledged, 18 fixed) including an h2o HTTP redirect-loop on encoded null bytes, stateless network-aware ABR for IPFS, energy-saving cooperative RSU sleep scheduling for V2I, V2

Why it matters

A reviewed impact interpretation has not been published for this record.

Evidence and limitations

Source ID
arxiv_cs_ni
Record identifier
aa4bd7cc6c1bf1a1e3cdd4c42bbbfdf47e942a38ae96186fad98e77cfc72b4c5
Enrichment time
2026-06-30T07:23:49Z
AI-assisted enrichment
Yes

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