CLOUD-D RF: Cloud-based Distributed Radio Frequency Heterogeneous Spectrum Sensing

In wireless communications, collaborative spectrum sensing is a process that leverages radio frequency (RF) data from multiple RF sensors to make more informed decisions and lower the overall risk of failure in distributed settings. However, most research in collaborative sensing focuses on homogeneous systems using identical sensors, which would not be the case in a real world wireless setting. Instead, due to differences in physical location, each RF sensor would see different versions of signals propagating in the environment, establishing the need for heterogeneous collaborative spectrum sensing. Hence, this paper explores the implementation of collaborative spectrum sensing across heterogeneous sensors, with sensor fusion occurring in the cloud for optimal decision making. We investigate three different machine learning-based fusion methods and test the fused model's ability to perform modulation classification, with a primary goal of optimizing for network bandwidth in regard to next-generation network applications. Our analysis demonstrates that our fusion process is able to optimize the number of features extracted from the heterogeneous sensors according to their varying performance limitations, simulating adverse conditions in a real-world wireless setting.