Qubeijian Wang;Shaojie Bai;Wen Sun;Wei Hu;Yalin Liu;Hong-Ning Dai;Zheng Yan
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引用次数: 0
Abstract
The rapid evolution of uncrewed aerial vehicles (UAVs) has positioned air-ground networks as vital infrastructures for diverse applications. However, the open channels of air-ground networks remain inherently vulnerable to persistent eavesdropping threats. While physical-layer key generation (PLKG) offers a lightweight security mechanism by leveraging channel reciprocity to extract shared secrets, the inherent mobility of UAVs introduces a paradoxical tradeoff. Increased channel randomness from dynamic flight patterns enhances security through entropy amplification but simultaneously disrupts channel reciprocity, leading to key mismatch between legitimate parties. Existing PLKG schemes struggle to maintain reliability in key generation due to static channel characteristics and synchronization overhead, limiting their practical deployment in air-ground networks. To resolve this conflict, we propose a Doppler shift key generation (DSKG) scheme that systematically regulates Doppler shifts through UAV trajectory design to derive secure keys. By formulating the problem as a Markov decision process, we develop a proximal policy optimization (PPO)-clip-based reinforcement learning algorithm to dynamically control UAV speed and steering angle, ensuring robust Doppler shift reciprocity while maximizing both key entropy and generation rate. Experimental results quantify the improvements of our scheme over benchmarks in maintaining high key unpredictability and generation efficiency. Furthermore, the analysis provides valuable insights into parameter impacts, confirming the practical viability of the DSKG scheme for securing air-ground communications.
期刊介绍:
The IEEE Transactions on Information Forensics and Security covers the sciences, technologies, and applications relating to information forensics, information security, biometrics, surveillance and systems applications that incorporate these features