Akademik Veri Yönetim Sistemi
TÜBİTAK Uluslararası İkili İşbirliği Projesi, 2529 - Pakistan Bilim ve Teknoloji Bakanlığı (MoST) İkili İşbirliği Programı, 2025 - 2027
The collaborating entities have culminated the basic principles and concepts of Deep Learning-Aided Radiofrequency Fingerprinting (DL-Aided RFF), utilizing state-of-the-art methods prevalent in the industry, into research publications. In alignment with the cited objectives, a lightweight deep learning model aimed at reduced inference latency has already been proposed by Turkish LPI. The simulation results demonstrate promising potential for further exploration and improvement. Additionally, dedicated efforts have been made to identify the challenges related to the sustainability and scalability of RFF, coupled with potential solutions. From a deployment framework perspective, a use case illustrating the suitability of RFF in Smart Grids has also been formulated by Turkish LPI. Collectively, these efforts position the state-of-the-art of collaborating entities at TRL-2. The next phase of research aims to achieve TRL-6 by advancing the technology toward practical deployment scenarios. To this end, there is a need to optimize key metrics including classification accuracy, openness, and inference latency for the implementation on low-resource edge devices.