We call for original and unpublished papers, which must be formatted in the standard IEEE two-column format that is used by the IEEE WCNC 2025 main conference, and must not exceed six pages in length (including references). All submitted papers will go through a strict peer review process, and all accepted papers that are presented by one of the authors at the workshop will be published in the IEEE WCNC 2025 workshop proceedings and IEEE Xplore.
Submission Deadline: 15 December 2024 (Extended)
Notification of Acceptance: 15 January 2025
Camera Ready: 1 February, 2025
Workshop: 24 March, 2025
Wireless security has attracted massive attention from academia and industry. There has been exponentially increasing number of wireless devices and pervasive integrations of wireless services into our everyday life. However, due to the broadcast nature of the wireless channels, securing wireless communications is extremely challenging. The security of wireless networks is currently protected by upper-layer cryptographic methods, but recently physical layer-based approaches have emerged as promising means to secure wireless transmissions.
Physical layer security (PLS) exploits the unique and random characteristics of wireless channels such as fading or noise to design secure transmission strategies, extract their randomness for key generation, and leverage the unique channel features for authentication. Over the past few years, PLS has been widely recognized as a key enabling technique for secure wireless communications in future networks. In addition, machine learning and deep learning have shown great potential to enhance PLS.
In line with such objectives, original contributions, for both technical and demo sessions, are solicited on topics of interest to include, but not limited to, the following:
Artificial intelligence-generated content (AIGC) for PLS
Large language model (LLM) for PLS
Application of machine learning and deep learning for PLS
Secure signal processing
Secure fundamental theory
Secure advanced spatial diversity techniques (secure cooperative communications, secure two-way cooperative communications, secure MIMO communications and secure cognitive radio systems)
PLS in the Internet of Things (IoT), 5G and 6G
Secret key generation and agreement
Covert and stealth wireless communications
Physical layer authentication using channel features
Radio frequency fingerprint identification using hardware impairments
PLS for massive MIMO systems, UAV-aided systems, and mm-wave/THz transmission
PLS for emerging technologies such as integrated sensing and communications, near-field communications and intelligent reflecting surface
Cross-layer designs for security
Prototype, practical testbeds, and performance evaluation for PLS
Prof. Xianbin Wang, Western University, Canada, xianbin.wang@uwo.ca
Prof. Kaushik Chowdhury, University of Texas at Austin, USA, kaushik@utexas.edu
Prof. F. Javier Lopez-Martinez, University of Granada, Spain, fjlm@ugr.es
Prof. Nan Yang, Australian National University, Australia, nan.yang@anu.edu.au
Dr. Junqing Zhang, University of Liverpool, UK, junqing.zhang@liverpool.ac.uk
Dr. Onur Günlü, Linköping University, Sweden, onur.gunlu@liu.se
Dr. Guyue Li, Southeast University, China, guyuelee@seu.edu.cn
Professor Stefano Tomasin, University of Padova, Italy
Title: Security and Privacy in 6G Systems: Problems and Solutions from the Physical Layer
Professor Stefano Tomasin, University of Padova, Italy
Title: Security and Privacy in 6G Systems: Problems and Solutions from the Physical Layer
Effect of IQ Imbalance on Cooperative Jamming Cancellation for 6G Security in Physical Layer
Against Half-Colluding Wardens: Covert Communication Performance Analysis and Transmit Power Optimization
Artificial Noise Aided Secure Transmission in Active RIS-Assisted CF System Without Eavesdroppers' CSI
Cell-Free Multi-Eavesdropper Detection Strategy Using Physical Layer Security
Secret Key Capacity of Key Generation in the Slow-Fading Wireless Channels
Ensemble Learning-Enhanced Radio Frequency Fingerprinting
Noise-Robust Radio Frequency Fingerprint Identification Using Denoise Diffusion Model
Large Language Model Enabled Lightweight RFFI for 6G Edge Intelligence