Multiuser Adversarial Attack on Deep Learning for OFDM Detection
نویسندگان
چکیده
Adversarial attack has been widely used to degrade the performance of deep learning (DL), especially in field communications. In this letter, we evaluate different white-box and black-box adversarial algorithms for a DL-based multiuser orthogonal frequency division multiplexing (OFDM) detector subject attack. The bit error rates under attacks are compared. results show that, perturbation efficiency is higher than conventional interference. Virtual methods (VAM) zeroth-order-optimization (ZOO) perform best among methods, respectively. They also effective when changes starting time. Additionally, adding number attackers found useful improve VAM but not ZOO. This letter shows that powerful generate against OFDM
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ژورنال
عنوان ژورنال: IEEE Wireless Communications Letters
سال: 2022
ISSN: ['2162-2337', '2162-2345']
DOI: https://doi.org/10.1109/lwc.2022.3207348