Exploiting Carrier Frequency Offset and Phase Noise for Physical Layer Authentication in UAV-Aided Communication Systems

This paper exploits two intrinsic hardware-specific fingerprints in terms of carrier frequency offset (CFO) and phase noise (PHN) to propose a two-dimensional physical layer authentication (PLA) scheme in the unmanned aerial vehicle (UAV)-aided communication systems. By leveraging expectation conditional maximization (ECM), extended Kalman filtering (EKF) algorithms and binary hypothesis testing, we first extract the inherent hardware impairments of UAV-aided systems including CFO and PHN as PHY-layer fingerprints to establish an authentication framework. To accurately characterize authentication performance, we examine the hybrid Cramér-Rao lower bound (HCRLB) for individual estimators of CFO and PHN, and then theoretically derive the analytical expressions for the false alarm and detection probabilities by utilizing tools from statistical signal processing. Finally, extensive numerical results are provided to validate the correctness of the developed theoretical models and to illustrate the authentication performance of the proposed scheme under various system parameters.

I. Introduction

Unmanned aerial vehicles (UAVs) integrated with terrestrial communication networks (TCNs) serves as a key propellent to meet the increasing demands of the ubiquitous wireless coverage and improved network capacity [1]. Such a flexible and maneuverable wireless network architecture is greatly promising to support lots of important applications in the daily information exchange, disaster relief, military troop communication, etc [2]. However, it is notable that the UAV-aided TCN system is very vulnerable to identity-based spoofing attacks because of properties such as the natural openness of the transmission medium and highly dynamic changes of the network topology [3].