DRL-Driven Edge-Aware Utility Optimization for Multi-Slice 6G Networks
2026-05-25T07:24:17Z•91fe2d71ffe86b45b3c10106cc533de18258b511fe7a4f8db635da61b5cab616
6GDQNDRL-securityGreen-IoTLPWANLTE-MLoRaLoRaWANMBRLLCMILPMonte-CarloNB-IoTO-RANSigfoxVRage-of-servicedeep-reinforcement-learningedge-cachingmagnetometermiotynetwork-slicingradarreceding-horizon-control','RHC' ,sensor-networkwildlife-vehicle-collision
What happened
This silver document aggregates recent arXiv submissions (May 25, 2026) across networking, IoT, wireless, and robotics. Key contributions: (1) a DRL/DQN-driven edge caching and dynamic resource allocation framework for 6G O-RAN multi-slice support of VR and MBRLLC slices; (2) a combined radar + three-axis magnetometer sensor network with LoRa-mediated coordination for wildlife-vehicle collision reduction (Monte Carlo evaluation); (3) an MILP and heuristic schedulers for energy-harvesting Green IoT optimizing sampling and age-of-service; (4) performance analysis of DCF/CSMA-CA in full-duplex Wi
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_cs_ni
- Record identifier
- 91fe2d71ffe86b45b3c10106cc533de18258b511fe7a4f8db635da61b5cab616
- Enrichment time
- 2026-05-25T07:24:17Z
- AI-assisted enrichment
- Yes
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