Window Function-Based Frequency Diverse Sub-Arrays for Randomized Radiation Enhancement
You, Byungha Lee, In-Ho Kim, Minseok Jung, Haejoon Guizani, Mohsen
You, Byungha
Lee, In-Ho
Kim, Minseok
Jung, Haejoon
Guizani, Mohsen
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Machine Learning
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Journal article
Date
2025
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English
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Abstract
When it comes to physical layer security (PLS), a combined technique of randomized radiation and frequency diverse sub-arrays (FDSAs) with nonlinear frequency offsets (FOs) can produce a mainlobe focused around the target area on an angle-range domain. Despite the high secrecy performance, randomizing on an FDSA-by-FDSA basis has limitations on the flexibility and secrecy performance due to its restrained randomness. In this paper, we propose to enhance the randomness of the randomized radiation technique exploiting FDSAs with window function-based FOs. An exact secrecy rate analysis is provided and shown to be coherent with the simulation results. Along with the validation of the proposed analysis, the secrecy performance of the proposed technique is evaluated by secrecy rate and vulnerable area, which are in conflicting relationships. Based on the characteristics, the window function setting is discussed.
Citation
B. You, I. -H. Lee, M. Kim, H. Jung and M. Guizani, "Window Function-Based Frequency Diverse Sub-Arrays for Randomized Radiation Enhancement," in IEEE Transactions on Vehicular Technology, doi: 10.1109/TVT.2025.3573616
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IEEE Transactions on Vehicular Technology
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IEEE