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

Published: August 20, 2024

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

Published: April 30, 2025

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

Saurabh Parkar

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