期刊
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
卷 39, 期 8, 页码 2305-2317出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSAC.2021.3087243
关键词
Specific emitter identification (SEI); complex-valued neural network (CVNN); sparse structure selection (Triple-S); knowledge distillation (KD)
资金
- Japan Society for the Promotion of Science (JSPS) KAKENHI [JP19H02142]
- U.S. National Science Foundation [CCr-1908308, CCF-1908308]
- Natural Sciences and Engineering Research Council of Canada (NSERC)
- National Science and Technology Major Project of the Ministry of Science and Technology of China [TC190A3WZ-2]
- National Natural Science Foundation of China [61901228]
- Summit of the Six Top Talents Program of Jiangsu [XYDXX-010]
- Program for High-Level Entrepreneurial and Innovative Team [CZ002SC19001]
- Project of the Key Laboratory of Universal Wireless Communications (BUPT) of Ministry of Education of China [KFKT-2020106]
- Open Project of the Shaanxi Key Laboratory of Information Communication Network and Security [ICNS201902]
SEI technology enhances wireless communication security by discriminating individual emitters, with the use of CVNN and network compression leading to significant performance improvement and reduced model complexity and size, demonstrating high potential for application in IoT scenarios.
Specific emitter identification (SEI) is a promising technology to discriminate the individual emitter and enhance the security of various wireless communication systems. SEI is generally based on radio frequency fingerprinting (RFF) originated from the imperfection of emitter's hardware, which is difficult to forge. SEI is generally modeled as a classification task and deep learning (DL), which exhibits powerful classification capability, has been introduced into SEI for better identification performance. In the recent years, a novel DL model, named as complex-valued neural network (CVNN), has been applied into SEI methods for directly processing complex baseband signal and improving identification performance, but it also brings high model complexity and large model size, which is not conducive to the deployment of SEI, especially in Internet-of-things (IoT) scenarios. Thus, we propose an efficient SEI method based on CVNN and network compression, and the former is for performance improvement, while the latter is to reduce model complexity and size with ensuring satisfactory identification performance. Simulation results demonstrated that our proposed CVNN-based SEI method is superior to the existing DL-based methods in both identification performance and convergence speed, and the identification accuracy of CVNN can reach up to nearly 100% at high signal-to-noise ratios (SNRs). In addition, SlimCVNN just has 10%similar to 30% model sizes of the basic CVNN, and its computing complexity has different degrees of decline at different SNRs; there is almost no performance gap between SlimCVNN and CVNN. These results demonstrated the feasibility and potential of CVNN and model compression.
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