Classification and control of cognitive radios using hierarchical neural network
Document Type
Conference Proceeding
Date of Original Version
12-1-2010
Abstract
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. © 2010 Springer-Verlag Berlin Heidelberg.
Publication Title, e.g., Journal
Lecture Notes in Electrical Engineering
Volume
67 LNEE
Citation/Publisher Attribution
Chen, Sheng, Xiaochen Li, Qiao Cai, Nansai Hu, Haibo He, Yu Dong Yao, and Joseph Mitola. "Classification and control of cognitive radios using hierarchical neural network." Lecture Notes in Electrical Engineering 67 LNEE, (2010). doi: 10.1007/978-3-642-12990-2_39.