Hung, Peter C. and McLoone, Sean F. and Farrell, Ronan
Direct and Indirect Classification of High Frequency LNA Gain Performance - A Comparison Between SVMs and MLPs.
International Journal of Computing, 8 (1).
The task of determining low noise amplifier (LNA) high-frequency performance in functional testing is as
challenging as designing the circuit itself due to the difficulties associated with bringing high frequency signals offchip.
One possible strategy for circumventing these difficulties is to inferentially estimate the high frequency
performance measures from measurements taken at lower, more accessible, frequencies. This paper investigates the
effectiveness of this strategy for classifying the high frequency gain of the amplifier, a key LNA performance parameter.
An indirect Multilayer Perceptron (MLP) and direct support vector machine (SVM) classification strategy are
considered. Extensive Monte-Carlo simulations show promising results with both methods, with the indirect MLP
classifiers marginally outperforming SVMs.
||Research presented in this paper was funded by
Enterprise Ireland Commercialisation Fund (EI
CFTD/2003/304) under the National Development
Plan. The authors gratefully acknowledge this
||High Frequency; Gain Performance; SVMs; MLPs; LNA; Functional testing; Classification; Support Vector Machines; Multilayer Perceptrons;
||Faculty of Science and Engineering > Electronic Engineering
Dr. Ronan Farrell
||23 Oct 2012 14:55
|Journal or Publication Title:
||International Journal of Computing
||Enterprise Ireland Commercialisation Fund
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