Cross-Domain Generalization in Optical Networks via Joint Contrastive and Classification Learning
2026-07-24T07:23:54Z•e7e08792b3ae3d1044e8dab5b5c4a45b55a85d7bc2bc67b50e6e2954c21f6322
6G / ISACIoT coexistenceLLM-in-the-loop network controlNR-V2XWMFMadversarial MLavailability/DoS riskdigital twindistributed control planeexplainable AI (XAI)fixed wireless access (FWA)graph neural networkslearning-to-optimizemachine learning robustnessmicrowave backhauloptical networksout-of-distribution detectionprivacy riskruntime verificationsafety-critical systemssatellite IABspectrum compliancesupply-chain/operational risk
What happened
This collection of network and ML research addresses robustness, efficiency, and orchestration across optical networks, 5G/6G wireless (IAB/FWA, NR-V2X, ISAC), IoT coexistence, and quantum networking. Key technical contributions include joint contrastive+classification representation learning for cross-domain generalization in optical networks; mobility-aware AoI minimization with an online learning scheduler for collaborative perception; an LLM-driven intent translation layer for satellite-integrated IAB to allocate temporary vs fixed rural connectivity; a Digital Twin–assisted energy‑aware,
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_cs_ni
- Record identifier
- e7e08792b3ae3d1044e8dab5b5c4a45b55a85d7bc2bc67b50e6e2954c21f6322
- Enrichment time
- 2026-07-24T07:23:54Z
- AI-assisted enrichment
- Yes
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