CALO: Constraint-Aware Learning Optimization for Joint Resource Allocation in Double-Active RIS-Assisted Wireless Networks
2026-07-01T07:23:51Z•034038f3337fa2c493243203bc5a7019311c8c2b582941a2d804256f47965dcc
5G RAN slicingLEO satellite orchestrationLLM-enabled edge networksTSN integrationbudget-adaptive routingconfigured grant schedulingcooperative drivingcovert communicationsdouble-active RISeavesdroppingedge-cloud offloadelectromagnetic leakagegraph neural networksjamminglow-latency networksprivacyprompt injectionprompt poisoningrelay-assisted semantic communicationsresource allocationsemantic leakagesemantic-aware multiple accessteleoperated driving
What happened
This collection of recent arXiv papers highlights both advances and new security/privacy risks across 5G/6G, edge/LLM-enabled networks, semantic communications, and vehicular/LEO systems. Key findings: (1) LLM-enabled edge networks (LLMENs) are exposed to eavesdropping, jamming, prompt poisoning/injection, and electromagnetic leakage from heavy LLM computation; the authors propose covert communications/computations to reduce detectability and overhead. (2) Relay-assisted semantic communications leak semantic meaning: intermediate relays operating on learned latent representations can infer and
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_cs_ni
- Record identifier
- 034038f3337fa2c493243203bc5a7019311c8c2b582941a2d804256f47965dcc
- Enrichment time
- 2026-07-01T07:23:51Z
- AI-assisted enrichment
- Yes
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