Digital Twin-assisted belief-state reinforcement learning for latency-robust ISAC in 6G networks
arXiv 2604.25967•f36e240a170ad5e01d8a0d773fe15597a999220e2d7f752395c625886c3a9adc
5G6GDPIISACMRSSNeuralEmuQoESWE-Bench-5GStreamGuardbenchmarkingdigital-twinedge-cloudfederated-learningnetwork-emulationpacket-markingprogressive-semantic-communicationreinforcement-learningscheduler-emulationspectrum-sharingsplit-learningtelemetry-latencytime-series-clusteringtraffic-matrixtwin-in-the-loopvision-language-models-VLMs','3D-radio-map','URF-GS','tactical-w
Paper metadata
- arXiv ID
- 2604.25967
- Version
- Not specified by this published record
- Category
- Computer Science — Networking and Internet Architecture (cs.NI)
The PDF link points to arxiv.org. Baitaphish does not expose a private stored PDF.
Evidence and limitations
- Source ID
- arxiv_cs_ni
- Record identifier
- f36e240a170ad5e01d8a0d773fe15597a999220e2d7f752395c625886c3a9adc
- Enrichment time
- 2026-04-30T07:23:53Z
- AI-assisted enrichment
- Yes
This record may overlap with other records. Its enrichment can be incomplete or wrong, and machine assistance was used. Validate consequential decisions against the linked source and your own environment.