Submit

Login Login

Register Register

Home / Journals / CMC / Online First / doi:10.32604/cmc.2025.061869
Journal Menu
Special Issues
Table of Content

All issues

Online First

2025

2024

2023

2022

2021

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

Open Access

ARTICLE

A Lightweight Convolutional Neural Network with Squeeze and Excitation Module for Security Authentication Using Wireless Channel

Xiaoying Qiu1,*, Xiaoyu Ma1, Guangxu Zhao1, Jinwei Yu2, Wenbao Jiang1, Zhaozhong Guo1, Maozhi Xu3
1 College of Computer Science, Beijing Information Science and Technology University, Beijing, 100192, China
2 School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, 100876, China
3 School of Mathematical Sciences, Peking University, Beijing, 100871, China
* Corresponding Author: Xiaoying Qiu. Email: email
(This article belongs to the Special Issue: Applications of Artificial Intelligence for Information Security)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.061869

Received 05 December 2024; Accepted 10 February 2025; Published online 07 March 2025

Abstract

Physical layer authentication (PLA) in the context of the Internet of Things (IoT) has gained significant attention. Compared with traditional encryption and blockchain technologies, PLA provides a more computationally efficient alternative to exploiting the properties of the wireless medium itself. Some existing PLA solutions rely on static mechanisms, which are insufficient to address the authentication challenges in fifth generation (5G) and beyond wireless networks. Additionally, with the massive increase in mobile device access, the communication security of the IoT is vulnerable to spoofing attacks. To overcome the above challenges, this paper proposes a lightweight deep convolutional neural network (CNN) equipped with squeeze and excitation module (SE module) in dynamic wireless environments, namely SE-ConvNet. To be more specific, a convolution factorization is developed to reduce the complexity of PLA models based on deep learning. Moreover, an SE module is designed in the deep CNN to enhance useful features and maximize authentication accuracy. Compared with the existing solutions, the proposed SE-ConvNet enabled PLA scheme performs excellently in mobile and time-varying wireless environments while maintaining lower computational complexity.

Keywords

Physical layer authentication; blockchain; squeeze and excitation module; computational cost; mobile scenario

  • 151

    View

  • 30

    Download

  • 0

    Like

Related articles

Share Link