Classification and Control of Cognitive Radios Using Hierarchical Neural Network

This paper proposes a method to protect the communication band through machine learning in cognitive networks. A machine learning cognitive radio (MLCR) extracts features from the signal waveforms received from various radios. A machine learning radio user (MLRU) assigns the states, i.e., unauthorized/authorized, and the associated actions, i.e., interfering/no interfering, to each waveform. The MLCR learns through a proposed hierarchical neural network to classify the signal states based on their features. The {signal, action} pairs are stored in the knowledge base and can be retrieved by MLCR automatically based on its prediction of the signal state related to the presented signal waveform. A case study of protecting the band of a legacy radio using our proposed method is provided to validate the effectiveness of this work.