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Information Criteria Based Optimal Structure Identification of RF Power Amplifier Models

Publication Type : Journal Article

Publisher : Journal of Intelligent and Fuzzy Systems, IOS Press .

Source : Journal of Intelligent and Fuzzy Systems, IOS Press, Volume 36, Number 3, p.2137-2145 (2018)

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Keywords : Combinatorial models, Complexity reduced, Cost functions, Gallium arsenide, III-V semiconductors, Information criterion, Integrated circuits, Intelligent systems, Novel information, Optimal structures, Parsimonious modeling, Particle swarm optimization (PSO), Power amplifiers, Radio frequency amplifiers, Radio frequency power amplifiers, RF power amplifiers, Soft computing, Structural optimization .

Campus : Bengaluru

School : School of Engineering

Department : Electronics and Communication

Year : 2018

Abstract : This article presents a novel information criterion based optimal model parameter selection algorithm for behavioral modeling of Radio Frequency Power Amplifiers (RF PAs). The proposed approach uses Particle Swarm Optimization (PSO) along with the Information Criterion (IC) based cost functions for determining the most parsimonious model from all the available combinatorial models. The proposed technique thereby helps in deriving complexity reduced models without compromising modeling accuracy. The validation of the proposed approach was carried out by modeling a GaAs based PA driven by a 20-MHz generic random input signal. It was shown that, the model performance was maintained while its complexity in terms of number of coefficients was reduced by around 35% in the considered cases. In addition, the proposed PSO based approach helps in deriving the most parsimonious PA model in a very short amount of time compared to the conventional sweep technique. © 2019 - IOS Press and the authors.

Cite this Research Publication : S. R. Bhavanam, Sanjika Devi R V, Mudulodu, S., and Dr. Dhanesh G. Kurup, “Information criteria based optimal structure identification of RF power amplifier models”, Journal of Intelligent and Fuzzy Systems, vol. 36, pp. 2137-2145, 2018.

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