ZTE Communications ›› 2025, Vol. 23 ›› Issue (1): 18-29.DOI: 10.12142/ZTECOM.202501004
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MENG Rui1, FAN Dayu1, XU Xiaodong1,2(), LYU Suyu3, TAO Xiaofeng4
Received:
2025-01-09
Online:
2025-03-25
Published:
2025-03-25
About author:
MENG Rui received his BS degree in information engineering and PhD degree in information and communication engineering both from Beijing University of Posts and Telecommunications (BUPT), China in 2018 and 2024, respectively. He is currently a postdoctoral fellow with BUPT. His research interests cover next-generation networks, physical layer authentication, identity security, semantic security, deep learning, and Internet of Things.MENG Rui, FAN Dayu, XU Xiaodong, LYU Suyu, TAO Xiaofeng. Endogenous Security Through AI-Driven Physical-Layer Authentication for Future 6G Networks[J]. ZTE Communications, 2025, 23(1): 18-29.
Parameters | Values | Parameters | Values |
---|---|---|---|
4 | 3 | ||
Number of RIS elements | 8 | Carrier frequency | 3.5 GHz |
3 | 4 | ||
Bandwidth | 1 MHz | Speed of users | 2 m/s |
Number of each user's CSI fingerprint samples | 50 000 | Number of each user’s CSI fingerprint sequences | 1 000 |
Length of each CSI fingerprint sequence | 50 | Ratio of training fingerprints | 0.6 |
Learning rate | 0.000 1 | Batch size | 16 |
Number of GNN layers | 3 | Ratio of pooling for nodes | 0.2 |
Table 2 Simulation parameters
Parameters | Values | Parameters | Values |
---|---|---|---|
4 | 3 | ||
Number of RIS elements | 8 | Carrier frequency | 3.5 GHz |
3 | 4 | ||
Bandwidth | 1 MHz | Speed of users | 2 m/s |
Number of each user's CSI fingerprint samples | 50 000 | Number of each user’s CSI fingerprint sequences | 1 000 |
Length of each CSI fingerprint sequence | 50 | Ratio of training fingerprints | 0.6 |
Learning rate | 0.000 1 | Batch size | 16 |
Number of GNN layers | 3 | Ratio of pooling for nodes | 0.2 |
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