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

TL;DR

The paper addresses security challenges in low-altitude wireless networks (LAWNs) arising from line-of-sight channels, mobility, and unlicensed spectrum. It advocates using large AI models (LAMs), including multi-modal and domain-adapted LAMs, to create proactive and robust secure-communication systems, notably via a chain-of-thought-enabled LLM to guide reinforcement learning. The authors present a CoT-LLM–enhanced optimization framework that generates enhanced state features and intrinsic rewards, validated by a case study showing improved convergence stability and higher secrecy rates. The work highlights directions for building domain-specific, privacy-preserving LAM deployments and trustworthy reasoning in adversarial LAWNs.

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.

Figures (4)

• Figure 1: A systematic overview of communication security threats to LAPs in LAWNs.
• Figure 2: Overview and domain adaptation of LAMs, as well as their use in enabling secure communications in LAWNs.
• Figure 3: CoT-LLM-enhanced state representation and reward function design for RL toward secure communications in LAWNs.
• Figure 4: Performance comparison for the proposed framework and baseline.