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NSF CyberTraining: O-RAN-Based Cyberinfrastructure for Future-Generation Wireless Communication and Sensing
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At Stevens Institute of Technology, we held a month-long, NSF CyberTraining workshop designed to train Masterโs students in next-generation wireless technologies. The program emphasized hands-on learning across key areas such as Software-Defined Radios (SDRs), mmWave sensing, Integrated Sensing and Communication (ISAC), and Open Radio Access Networks (O-RAN). Through intensive weekly sessions covering the theoretical concepts and practical applications, participants engaged directly with real-world experimental platforms to build both foundational knowledge and technical fluency in advanced wireless systems. Read more
news
2024 06 28 Permalink
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๐ Awarded 1st Place at the ECE Research Scholarship, Spring 2024. Read more
2024 08 13 Permalink
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๐ก Presented a poster on Federated Learning for RF Fingerprinting in Open Radio Access Networks (O-RAN)โ at the 1st Symposium on Emerging Topics in Networks, Systems, and Cybersecurityโ. Read more
2025 04 30 Permalink
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๐ Successfully defended my Masterโs Thesis. Read more
2025 05 15 Permalink
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๐ง Iโll be at the 1st iCNS/ECE Symposium on AI Research and Innovations (DuckAI 2025โ). Read more
2025 05 20 Permalink
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๐ Graduated with a Master of Science in Applied Artificial Intelligence. Read more
2025 06 04 Permalink
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๐งโ๐ซNSF CyberTraining Summer 2025 begins at Stevens Institute of Technology. Read more
projects
Image Generation using Generative Adversarial Networks (GAN) Permalink
PyTorch-based GAN trained on CIFAR-10 for image synthesis Read more
Semi-Supervised Water Boundary Detection using Drone Imagery Permalink
SVM-C and K-means based classifier for water-land segmentation. Read more
research
Federated RF Fingerprinting for O-RAN Permalink
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Open Radio Access Network (O-RAN) offers a transformative approach to cellular network design by promoting a virtualized, open, and intelligent architecture. The increasing complexity and security demands of modern cellular networks necessitate robust methods for device identification and management. This paper provides a way for integrating Federated Learning for device fingerprinting within the Open Radio Access Network (O-RAN) framework, enhancing network security and device management. Our approach leverages unique RF signal characteristics, captured through Channel State Information (CSI), to identify devices without the need for centralized data processing or custom hardware. We set up a real-world experimental environment using the POWDER Wireless testbed, simulating O-RAN with base stations and user equipment. Using a deep learning model to process the CSI data to classify devices. Read more
V2X Network Slicing Prediction with Deep Learning. Permalink
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The rapid advancement of connected and autonomous vehicles has significantly increased the demand for efficient and reliable Vehicle-to-Everything (V2X) communication systems, which are essential for ensuring real-time data exchange between vehicles, infrastructure, and other entities. Network slicing is a technique that creates multiple virtual networks optimized for specific services, and is important in addressing the diverse Quality of Service (QoS) requirements of V2X communication, such as ultra-low latency, high bandwidth, and reliability. However, traditional cellular networks often struggle to meet these demands due to their static and inflexible architectures. The introduction of the Open Radio Access Network (O-RAN) architecture addresses these challenges by incorporating intelligent controllers, specifically the Near-Real-Time RAN Intelligent Controller (Near- RT RIC), which enhances network slicing through dynamic and adaptive management of network resources. Read more
5G-FMCW ISAC for Contactless Respiration Monitoring Permalink
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Accurate detection of respiratory patterns is critical for the early diagnosis of respiratory disorders, timely medical intervention, and long-term health monitoring. Conventional respiration monitoring techniques typically rely on specialized medical equipment and trained personnel, which constrains their applicability for real-time use in home care and self-monitoring. Recent advancements in contactless vital sign sensing have enabled the monitoring of key physiological indicators, such as respiration, without the need for invasive instrumentation. Among these, millimeter-wave (mmWave) technologies have shown promise for non-intrusive respiratory monitoring. However, existing mmWave-based approaches often require dedicated radar hardware and exclusive spectrum resources, thereby limiting their adaptability across diverse and resource-constrained environments. Read more