TY - GEN
T1 - Classification and control of cognitive radios using hierarchical neural network
AU - Chen, Sheng
AU - Li, Xiaochen
AU - Cai, Qiao
AU - Hu, Nansai
AU - He, Haibo
AU - Yao, Yu Dong
AU - Mitola, Joseph
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
KW - Cognitive Radio
KW - Hierarchical Neural Network
KW - Knowledge Base
KW - Machine Learning
UR - http://www.scopus.com/inward/record.url?scp=80052729182&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80052729182&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-12990-2_39
DO - 10.1007/978-3-642-12990-2_39
M3 - Conference contribution
AN - SCOPUS:80052729182
SN - 9783642129896
T3 - Lecture Notes in Electrical Engineering
SP - 347
EP - 353
BT - Advances in Neural Network Research and Applications
T2 - 7th International Symposium on Neural Networks, ISNN 2010
Y2 - 6 June 2010 through 9 June 2010
ER -