UAV-Enabled Wireless Networks for Integrated Sensing and Learning-Oriented Communication

被引：0

作者：

Zhuang, Wenhao ^{[1
]}

He, Xinyu ^{[1
]}

Mao, Yuyi ^{[1
]}

Liu, Juan ^{[2
]}

机构：

[1] Hong Kong Polytech Univ, Dept Elect & Elect Engn, Hong Kong, Peoples R China

[2] Ningbo Univ, Sch Elect Engn & Comp Sci, Ningbo 315211, Peoples R China

来源：

IEEE WIRELESS COMMUNICATIONS LETTERS | 2025年 / 14卷 / 02期

关键词：

Autonomous aerial vehicles; Internet of Things; Training; Wireless networks; Uplink; Servers; Integrated sensing and communication; Data models; Resource management; Signal to noise ratio; Integrated sensing and communication (ISAC); learning-oriented communication; edge artificial intelligence (AI); unmanned aerial vehicle (UAV); EDGE; DESIGN;

D O I：

10.1109/LWC.2024.3501395

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Future wireless networks are envisioned to support both sensing and artificial intelligence (AI) services. However, conventional integrated sensing and communication (ISAC) networks may not be suitable due to the ignorance of diverse task-specific data utilities in different AI applications. In this letter, a full-duplex unmanned aerial vehicle (UAV)-enabled wireless network providing sensing and edge learning services is investigated. To maximize the learning performance while ensuring sensing quality, a convergence-guaranteed iterative algorithm is developed to jointly determine the uplink time allocation, as well as UAV trajectory and transmit power. Simulation results show that the proposed algorithm significantly outperforms the baselines and demonstrate the critical tradeoff between sensing and learning performance.

引用

页码：340 / 344

页数：5

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