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

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Abstract

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.

Item Type:	Article
Additional Information:	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 support.
Keywords:	High Frequency; Gain Performance; SVMs; MLPs; LNA; Functional testing; Classification; Support Vector Machines; Multilayer Perceptrons;
Academic Unit:	Faculty of Science and Engineering > Electronic Engineering
Item ID:	2721
Depositing User:	Ronan Farrell
Date Deposited:	23 Oct 2012 14:55
Journal or Publication Title:	International Journal of Computing
Refereed:	No
Funders:	Enterprise Ireland Commercialisation Fund
URI:	http://computingonline.net/eng/index.php
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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