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) frame-work 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 lo...
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| Veröffentlicht in: | IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications workshops (Print) S. 1 - 6 |
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| Sprache: | Englisch |
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IEEE
05.09.2023
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| ISSN: | 2166-9589 |
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| Abstract | In this paper, we present a novel unmanned aerial vehicle (UAV) aided physical layer authentication (PLA) frame-work 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. |
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| AbstractList | In this paper, we present a novel unmanned aerial vehicle (UAV) aided physical layer authentication (PLA) frame-work 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. |
| Author | Ma, Zheng Zhou, Yi Li, Yonghui Liu, Heng Yeoh, Phee Lep Vucetic, Branka |
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| Snippet | In this paper, we present a novel unmanned aerial vehicle (UAV) aided physical layer authentication (PLA) frame-work to detect the origin of the received... |
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| SubjectTerms | Authentication Benchmark testing false alarm probability miss detection probability Physical layer authentication Probability density function Programmable logic arrays Radio transmitters Simulation UAV communications Urban areas |
| Title | Signal-To-Noise Ratio Based Physical Layer Authentication in UAV Communications |
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