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

Yankun WANG; Dengke GUO; Dongtang MA; Jun XIONG; Xiaoying ZHANG

doi:10.11959/j.issn.1000-0801.2023240

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

Research and Development | 更新时间：2024-06-05

- Multi-user physical layer authentication mechanism based on lightweight CNN and channel feature assistance
- Telecommunications Science Vol. 39, Issue 11, Pages: 69-79(2023)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2023240
  CLC： TN92
- Published Online：2023-11，
  
  Published：20 November 2023
- 稿件说明：
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Yankun WANG, Dengke GUO, Dongtang MA, et al. Multi-user physical layer authentication mechanism based on lightweight CNN and channel feature assistance[J]. Telecommunications science, 2023, 39(11): 69-79.
DOI：

Yankun WANG, Dengke GUO, Dongtang MA, et al. Multi-user physical layer authentication mechanism based on lightweight CNN and channel feature assistance[J]. Telecommunications science, 2023, 39(11): 69-79. DOI： 10.11959/j.issn.1000-0801.2023240.

摘要

针对目前物理层的用户认证算法存在的鲁棒性差、复杂度高等问题，提出了一种轻量级卷积神经网络（CNN）信道特征提取算法，通过改变网络输入形式减少训练所需要的信道状态响应，同时基于该算法建立了一种多用户物理层信道特征辅助的认证机制，设计了从用户注册到认证的详细过程，并在线完成多用户认证及网络参数更新。仿真结果表明，所提算法能够完成多用户身份认证，在较小的训练轮次下获得良好的检测性能，且比现有的多用户认证算法需要的训练样本少。

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.

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references

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