基于轻量级CNN和信道特征辅助的多用户物理层认证机制

王延坤; 郭登科; 马东堂; 熊俊; 张晓瀛

doi:10.11959/j.issn.1000-0801.2023240

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基于轻量级CNN和信道特征辅助的多用户物理层认证机制

研究与开发 | 更新时间：2024-06-05

- 基于轻量级CNN和信道特征辅助的多用户物理层认证机制
- Multi-user physical layer authentication mechanism based on lightweight CNN and channel feature assistance
- 电信科学 2023年39卷第11期页码：69-79
- 作者机构：
- 作者简介：
  
  [ "王延坤（2000-），男，国防科技大学电子科学学院硕士生，主要研究方向为物理层安全、信道探测与建模等" ]
  [ "郭登科（1996- ），男，国防科技大学电子科学学院博士生，主要研究方向为无线物理层安全、智能超表面技术等" ]
  [ "马东堂（1969- ），男，博士，国防科技大学电子科学学院教授、博士生导师，主要研究方向为智能无线通信与网络、物理层安全、无人机通信与网络优化等" ]
  [ "熊俊（1987- ），男，博士，国防科技大学电子科学学院副研究员、硕士生导师，主要研究方向为智能无线通信、物理层安全、分布式协同等" ]
  [ "张晓瀛（1980- ），女，博士，国防科技大学电子科学学院副教授、硕士生导师，主要研究方向为宽带移动通信接收、信道探测与建模等" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(61372099);国家自然科学基金资助项目;The National Natural Science Foundation of China(61931020)
- DOI：10.11959/j.issn.1000-0801.2023240
  中图分类号： TN92
- 网络出版日期：2023-11，
  
  纸质出版日期：2023-11-20
- 稿件说明：
移动端阅览
王延坤, 郭登科, 马东堂, 等. 基于轻量级CNN和信道特征辅助的多用户物理层认证机制[J]. 电信科学, 2023,39(11):69-79.

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.
王延坤, 郭登科, 马东堂, 等. 基于轻量级CNN和信道特征辅助的多用户物理层认证机制[J]. 电信科学, 2023,39(11):69-79. DOI： 10.11959/j.issn.1000-0801.2023240.

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.

关键词

Keywords

references

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