Classification and control of cognitive radios using hierarchical neural network

Sheng Chen, Xiaochen Li, Qiao Cai, Nansai Hu, Haibo He, Yu Dong Yao, Joseph Mitola

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

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.

Original languageEnglish
Title of host publicationAdvances in Neural Network Research and Applications
Pages347-353
Number of pages7
DOIs
StatePublished - 2010
Event7th International Symposium on Neural Networks, ISNN 2010 - Shanghai, China
Duration: 6 Jun 20109 Jun 2010

Publication series

NameLecture Notes in Electrical Engineering
Volume67 LNEE
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

Conference7th International Symposium on Neural Networks, ISNN 2010
Country/TerritoryChina
CityShanghai
Period6/06/109/06/10

Keywords

  • Cognitive Radio
  • Hierarchical Neural Network
  • Knowledge Base
  • Machine Learning

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