We call for original and unpublished papers, which must be formatted in the standard IEEE two-column format that is used by the IEEE PIMRC 2025 main conference, and must not exceed six pages in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 PIMRC 2025 workshop proceedings and IEEE Xplore.
Please submit your papers in PDF format via edas (submission link to be provided soon).
Submission Deadline: June 15, 2025 27 Jun. 2025 (EXTENDED)
Notification of Acceptance: July 20, 2025
Camera Ready: August 3, 2025
Workshop: September 1-2 , 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. Stefano Tomasin, University of Padova, Italy stefano.tomasin@unipd.it
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
Dr. Haji M. Furqan, Vestel, Turkey, haji.madni@vestel.com.tr
TBC
TBC