Cloud- and Big Data-based Algorithms for Cognitive Radio Networks
Some of the challenges of Cognitive Radio Networks (CRNs) include the service interruption loss, complexity of processing and exchange of large amount of data and the non-real-time exchange of spectrum sensing data. We investigated a cloud-based Cooperative Spectrum Sensing model for CRN that uses a two-layer Fusion Center design for data aggregation and analysis. Our Data Analytics machine learning and optimization approaches include Directed Acyclic Graph and ensemble machine learning classifications.
