AI-Assisted ISAC Localization-as-a-Service for 6G UAV-IoT Networks
TL;DR
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.
Abstract
Integrated sensing and communication (ISAC) can enable sixth-generation (6G) unmanned aerial vehicle-assisted Internet of Things (UAV-IoT) networks to provide reliable Localization-as-a-Service (LaaS), but activating all aerial/terrestrial anchors and beams increases pilot overhead, energy use, and beam-training delay. This article proposes artificial intelligence (AI)-assisted ISAC resource selection for LaaS (AIRS-LaaS), an edge-intelligent framework that ranks candidate anchor--beam pairs using line-of-sight (LoS) likelihood, signal-to-interference-plus-noise ratio (SINR), sensing confidence, geometry, mobility risk, and resource cost. A lightweight selector then activates only a compact subset before localization. Simulations compare AIRS-LaaS with all-anchor, Fisher information matrix/Cram\'er--Rao lower bound (FIM/CRLB)-greedy, strongest-SINR, nearest-anchor, and random schemes under LoS/non-line-of-sight (NLoS) conditions and UAV mobility. 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.