面向6G无人机物联网网络的AI辅助ISAC定位即服务
AI-Assisted ISAC Localization-as-a-Service for 6G UAV-IoT Networks
AI总结：
针对6G UAV-IoT网络中ISAC LaaS的锚点激活开销问题，提出AIRS-LaaS边缘智能框架，通过多维度排序选择紧凑锚点-波束子集，实现定位、通信与开销的平衡。
AI中文摘要：
集成感知与通信（ISAC）可使第六代（6G）无人机辅助物联网（UAV-IoT）网络提供可靠的定位即服务（LaaS），但激活所有空中/地面锚点和波束会增加导频开销、能耗及波束训练延迟。本文提出面向LaaS的AI辅助ISAC资源选择方案（AIRS-LaaS），这是一种边缘智能框架，利用视距（LoS）概率、信干噪比（SINR）、感知置信度、几何条件、移动性风险及资源成本对候选锚点-波束对进行排序。随后，一个轻量级选择器仅激活紧凑子集用于定位。仿真在LoS/非视距（NLoS）条件及无人机移动场景下，将AIRS-LaaS与全锚点、费舍尔信息矩阵/克拉美罗下界（FIM/CRLB）贪心、最强SINR、最近锚点及随机方案进行对比。结果表明该方案实现了定位-通信-开销的平衡权衡，讨论部分还强调了标准驱动的关键性能指标（KPIs）、ISAC报告、定位置信度、 fallback操作、AI模型管理及隐私感知数据交换等内容。
英文摘要：
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/Cramér--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.
