基于轻量级CNN和信道特征辅助的多用户物理层认证机制

doi:10.11959/j.issn.1000-0801.2023240

Abstract

Abstract:

To address the problems of poor robustness and high complexity of current physical layer user authentication algorithms, a lightweight convolutional neural network (CNN) channel feature extraction algorithm was proposed to reduce the channel state response required for training by changing the form of network input, and a multi-user physical layer channel feature-assisted authentication mechanism was established based on this algorithm to design a detailed process from user registration to authentication, and multi-user authentication and network parameter update online were completed.Simulation results show that the proposed algorithm can complete multi-user authentication, obtain good detection performance with smaller training rounds, and require fewer training samples than existing multi-user authentication algorithms.

Key words: physical layer security, multi-user authentication, lightweight, CIR, robustness

CLC Number:

TN92

Yankun WANG, Dengke GUO, Dongtang MA, Jun XIONG, Xiaoying ZHANG. Multi-user physical layer authentication mechanism based on lightweight CNN and channel feature assistance[J]. Telecommunications Science, 2023, 39(11): 69-79.

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References 18

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层	维度	参数量	激活函数
输入层	2×100×1	—	—
卷积层	1×41×16	640	ReLU
卷积层	1×19×32	2 048	ReLU
全局平均池化层	32	—	—
全连接层	16	528	—
全连接层	3	51	softmax

Multi-user physical layer authentication mechanism based on lightweight CNN and channel feature assistance

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