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Numerical results show that the proposed ISAC-TM-MAE improves localization accuracy, resource efficiency, scalability, and robustness compared with centralized, distributed, and benchmark TDOA localization schemes.
Results show a balanced localization--communication--overhead tradeoff, while the discussion highlights standard-driven key performance indicators (KPIs), ISAC reporting, localization confidence, fallback operation, AI model management, and privacy-aware data exchange.
Simulation results demonstrate that RESCUE-ISAC improves energy efficiency, link reliability, sensing performance, mobility robustness, and runtime–performance trade-off compared with heuristic, lightweight, and optimization-based benchmark schemes.
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