---
格式版本: 2
标题: "AI-Assisted ISAC Localization-as-a-Service for 6G UAV-IoT Networks"
原文链接: "https://arxiv.org/abs/2608.10571"
发布日期: "2026-08-11"
发布时间校准状态: "found"
发布时间需复核: "否"
发布时间来源: "rule:local:strict_original_body"
发布时间证据: "**\\[v1\\]** Tue, 11 Aug 2026 07:00:11 UTC (5,480 KB)"
发布时间校准原因: "规则确认唯一严格发布时间，来源 local:strict_original_body"
发布时间校准置信度: "high"
发布时间候选数量: 18
发布时间严格候选数量: 6
发布时间原页读取状态: "source template page reused from URL open"
发布时间未找到原因: ""
发布时间校准时间: "2026-08-12T11:31:11+08:00"
发布时间仲裁状态: "skipped"
发布时间仲裁尝试次数: 0
发布时间仲裁耗时毫秒: 0
发现时间: "2026-08-12T11:26:18+08:00"
入库时间: "2026-08-12T03:31:11.740Z"
来源平台: "arXiv 学术论文搜索"
搜索渠道: "source_template"
搜索词: "https://arxiv.org/search/?query=AI&searchtype=all"
匹配关键词:
  - "AI"
  - "performance"
相关厂家:
  []
相关专家:
  []
内容类型: "网页"
抓取工具: "Free Fetch + Defuddle"
清洗工具: "Defuddle Markdown + Defuddle/Readability 正文提取"
原始附件:
  []
AI优质: "否"
AI打分: 0
AI分档: "非优质"
AI质检状态: "不通过"
AI打分理由: "论文主题为6G UAV-IoT网络的ISAC定位，与超节点/AI Rack/机柜级AI基础设施等核心关注点完全无关。"
AI质检模型: "ali-deepseek-v4-flash"
AI质检时间: "2026-08-12T11:31:19+08:00"
AI主题相关性: 0
AI来源权威性: 0
AI新颖性: 0
AI技术细节: 0
AI商业部署信号: 0
AI完整性: 0
采集批次: "2026年8月12日2点04分20秒"
采集批次ID: "20260812-020420-289"
去重键: "https://arxiv.org/abs/2608.10571"
---

## Electrical Engineering and Systems Science > Signal Processing

## Title:AI-Assisted ISAC Localization-as-a-Service for 6G UAV-IoT Networks

Authors:[Ruhul Amin Khalil](https://arxiv.org/search/eess?searchtype=author&query=Khalil,+R+A)

[View PDF](https://arxiv.org/pdf/2608.10571) [HTML (experimental)](https://arxiv.org/html/2608.10571v1)

> 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/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.

| Comments: |  |
| --- | --- |
| Subjects: | Signal Processing (eess.SP) |
| MSC classes: | 94A05, 94A12, 94A13, 94A40, 68M10, 68T05, 68T07, 90B18 |
| ACM classes: | C.2.1; C.2.3; C.2.4; I.2.6; C.3 |
| Cite as: | [arXiv:2608.10571](https://arxiv.org/abs/2608.10571) \[eess.SP\] |
|  | (or [arXiv:2608.10571v1](https://arxiv.org/abs/2608.10571v1) \[eess.SP\] for this version) |
|  | [https://doi.org/10.48550/arXiv.2608.10571](https://doi.org/10.48550/arXiv.2608.10571) |

## Submission history

From: Ruhul Amin Khalil Dr. \[[view email](https://arxiv.org/show-email/3ddc9593/2608.10571)\]  
**\[v1\]** Tue, 11 Aug 2026 07:00:11 UTC (5,480 KB)

[Which authors of this paper are endorsers?](https://arxiv.org/auth/show-endorsers/2608.10571) | Disable MathJax ([What is MathJax?](https://info.arxiv.org/help/mathjax.html))
