Agentic Semantic Sensing for Resource-Adaptive AI-RAN
We propose Agentic SemS, a closed-loop framework for AI-enabled radio access networks (AI-RANs) that controls sensing within a communication-feasible profile set.
ProofPaper ↗
Key points
- Semantic sensing (SemS) acquires task-relevant information rather than reconstructing complete physical information.
- A profile-conditioned causal Transformer updates the semantic belief from streaming observations, while key-value caching enables efficient state updates across profile changes without repeatedly processing the complete history.
- A semantic utility network estimates the task-level benefit of acquiring the next observation block under each feasible profile after accounting for sensing cost.
- Experiments on Widar3.0 with six emulated sensing profiles show that, in comparison with full-sequence High, the resource-efficient Agentic setting reduces normalized cumulative sensing cost by 25.33% while achieving 85.79% Macro-F1.
Sources (1)
- [1]Agentic Semantic Sensing for Resource-Adaptive AI-RANarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 6, 06:27 AM
We propose Agentic SemS, a closed-loop framework for AI-enabled radio access networks (AI-RANs) that controls sensing within a communication-feasible profile set.
Semantic sensing (SemS) acquires task-relevant information rather than reconstructing complete physical information.
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