Demo: CLOUD-D RF - Cloud-based Distributed Spectrum Sensing with Heterogeneous Hardware Testbed

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.