Demo: CLOUD-D RF - Cloud-based Distributed Spectrum Sensing with Heterogeneous Hardware Testbed
Year: 2025
Type: conference-paper
Abstract: Collaborative spectrum sensing enables distributed RF devices to share observations and improve classification performance in congested or contested environments. While most prior research assumes homogeneous sensors, real-world systems involve heterogeneous devices with diverse hardware, bandwidth, and channel conditions. This work introduces CLOUD-D RF, a framework that leverages convolutional neural networks at edge radios to extract learned features that are sent to a cloud-based fusion center for improved modulation classification. Phase I validated the concept with synthetic datasets, achieving over 97% classification accuracy. Phase II implemented a hardware testbed using GNU Radio with USRP radios, demonstrating real-time modulation classification over a distributed edge network of radios. Results confirm that cloud-based fusion reduces bandwidth requirements while maintaining accuracy, offering a practical path toward resilient and scalable spectrum sensing systems.
Authors: Rami Ghaleb, Jason Cox, Joseph Risi, Alyse M. Jones
Institutions: Virginia Tech, University of Virginia, Amazon (United States)
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4416955781