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Direct and Indirect Classification of High Frequency LNA Gain Performance - A Comparison Between SVMs and MLPs
Hung, Peter C.; McLoone, Sean F.; Farrell, Ronan
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.
Keyword(s): Electronic Engineering; High Frequency; Gain Performance; SVMs; MLPs; LNA; Functional testing; Classification; Support Vector Machines; Multilayer Perceptrons
Publication Date:
2009
Type: Journal article
Peer-Reviewed: No
Institution: Maynooth University
Citation(s): Hung, Peter C. and McLoone, Sean F. and Farrell, Ronan (2009) Direct and Indirect Classification of High Frequency LNA Gain Performance - A Comparison Between SVMs and MLPs. International Journal of Computing, 8 (1). pp. 24-31. ISSN 1727-6209
File Format(s): application/pdf
Related Link(s): http://eprints.maynoothuniversity.ie/2721/1/PH_Direct_Indirect.pdf
First Indexed: 2014-09-20 05:04:46 Last Updated: 2014-09-20 05:04:46