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Fault tolerant machine learning for nanoscale cognitive radio

Research output: Contribution to journalArticleScientificpeer-review

Details

Original languageEnglish
Pages (from-to)753-764
Number of pages12
JournalNeurocomputing
Volume74
Issue number5
DOIs
Publication statusPublished - Feb 2011
Publication typeA1 Journal article-refereed

Abstract

We introduce a machine learning-based classifier that identifies free radio channels for cognitive radio. The architecture is designed for nanoscale implementation, under nanoscale implementation constraints; we do not describe all physical details but believe future physical implementation to be feasible. The system uses analog computation and consists of cyclostationary feature extraction and a radial basis function network for classification. We describe a model for nanoscale faults in the system, and simulate experimental performance and fault tolerance in recognizing WLAN signals, under different levels of noise and computational errors. The system performs well under expected non-ideal manufacturing and operating conditions.

Keywords

  • Cognitive radio, Fault tolerance, Nanoelectronics, Nanotechnology, Radial basis function network