Low‐Interception Waveforms: To Prevent the Recognition of Spectrum Waveform Modulation via Adversarial Examples

Author:

Tan Jia12ORCID,Xie Haidong3,Zhang Xiaoying2,Ji Nan2,Liao Haihua2,Yu ZuGuo1,Xiang Xueshuang2ORCID,Liu Naijin2

Affiliation:

1. School of Mathematics and Computational Science Xiangtan University Xiangtan China

2. Qian Xuesen Laboratory of Space Technology China Academy of Space Technology Beijing China

3. China Academy of Aerospace Science and Innovation Beijing China

Abstract

AbstractDeep learning is applied to many complex tasks in the field of wireless communication, such as modulation recognition of spectrum waveforms, because of its convenience and efficiency. This leads to the problem of a malicious third party using a deep learning model to easily recognize the modulation format of the transmitted waveform. Some existing works address this problem directly using the concept of adversarial examples in the computer vision field without fully considering the characteristics of the waveform transmission in the physical world. Therefore, we propose two low‐interception waveforms (LIWs) generation methods, the LIW and ULIW algorithms, which can reduce the probability of the modulation being recognized by a third party without affecting the reliable communication of the friendly party. Among them, ULIW improves LIW algorithm by simulating channel noise during training cycle, and substantially reduces the perturbation magnitude while maintaining low interception accuracy. Our LIW and ULIW exhibit significant low‐interception performance in both numerical simulations and hardware experiments.

Publisher

American Geophysical Union (AGU)

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