Signal-To-Noise Ratio Based Physical Layer Authentication in UAV Communications

In this paper, we present a novel unmanned aerial vehicle (UAV) aided physical layer authentication (PLA) framework to detect the origin of the received signal between a legitimate transmitter and a malicious adversary, based on the physical properties of channel characteristics and geographical locations. First, we model the authentication hypothesis test at the UAV based on the signal-to-noise ratio (SNR) of each transmission and analyze the probability density functions (PDFs) of SNR differences. Then, we derive the explicit expressions of false alarm probability (FAP) and miss detection probability (MDP), both of which depict the occurrence of detection error. Next, with the aim of minimizing the MDP subject to a given FAP constraint, the detection threshold and UAV deployment are jointly optimized. Numerical results verify the accuracy of our derived expressions and demonstrate the impact of distribution rate and adversary's location on the detection performance. Moreover, numerical results also highlight the superiority of our proposed solution using SNR differences over benchmark strategy in high-rise urban environment.