Large AI Model-Enabled Secure Communications in Low-Altitude Wireless Networks: Concepts, Perspectives and Case Study

• URL: http://arxiv.org/abs/2508.00256v1
• Date: Fri, 01 Aug 2025 01:53:58 GMT
• Title: Large AI Model-Enabled Secure Communications in Low-Altitude Wireless Networks: Concepts, Perspectives and Case Study
• Authors: Chuang Zhang, Geng Sun, Jiacheng Wang, Yijing Lin, Weijie Yuan, Sinem Coleri, Dusit Niyato, Tony Q. S. Quek,
• Abstract summary: Low-altitude wireless networks (LAWNs) have the potential to revolutionize communications by supporting a range of applications.<n>We investigate some large artificial intelligence model (LAM)-enabled solutions for secure communications in LAWNs.<n>To demonstrate the practical benefits of LAMs for secure communications in LAWNs, we propose a novel LAM-based optimization framework.
• Score: 92.15255222408636
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Low-altitude wireless networks (LAWNs) have the potential to revolutionize communications by supporting a range of applications, including urban parcel delivery, aerial inspections and air taxis. However, compared with traditional wireless networks, LAWNs face unique security challenges due to low-altitude operations, frequent mobility and reliance on unlicensed spectrum, making it more vulnerable to some malicious attacks. In this paper, we investigate some large artificial intelligence model (LAM)-enabled solutions for secure communications in LAWNs. Specifically, we first explore the amplified security risks and important limitations of traditional AI methods in LAWNs. Then, we introduce the basic concepts of LAMs and delve into the role of LAMs in addressing these challenges. To demonstrate the practical benefits of LAMs for secure communications in LAWNs, we propose a novel LAM-based optimization framework that leverages large language models (LLMs) to generate enhanced state features on top of handcrafted representations, and to design intrinsic rewards accordingly, thereby improving reinforcement learning performance for secure communication tasks. Through a typical case study, simulation results validate the effectiveness of the proposed framework. Finally, we outline future directions for integrating LAMs into secure LAWN applications.

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