Comparison of hill-climbing and genetic algorithms for digital predistortion models sizing

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F16%3APU120890" target="_blank" >RIV/00216305:26220/16:PU120890 - isvavai.cz</a>

Result on the web

<a href="https://ieeexplore.ieee.org/document/7841189" target="_blank" >https://ieeexplore.ieee.org/document/7841189</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICECS.2016.3046" target="_blank" >10.1109/ICECS.2016.3046</a>

Result language

angličtina

Original language name

Comparison of hill-climbing and genetic algorithms for digital predistortion models sizing

Original language description

Generalized Memory Polynomial (GMP) models are widely used for the linearization of power amplifiers. They offer a good tradeoff between linearization performance and implementation complexity. Their structure is defined by 8 integer parameters representing different non-linearity orders and memory lengths. These 8 degrees of freedom allow achieving very good linearization performance with a small number of coefficients. But the optimal sizing (determination of the 8 parameters) of such models could require huge computation, for instance, if these 8 parameters are bounded between 1 and 10, there are 108 models to test using an exhaustive search, which is very computationally heavy and time consuming. Therefore optimization algorithms are needed to search for a GMP model structure which provides a good tradeoff between modeling accuracy and complexity. In this paper, we compare two heuristic optimization algorithms, hillclimbing and integer genetic algorithms, in terms of convergence speed, and optimality of the obtained solution regarding the defined criterion. They are evaluated using data measurements from an LDMOS Doherty Power Amplifier dedicated to base stations. The results show that both algorithms allow decreasing very significantly the searching time while giving optimal or close to optimal solutions. Compared with hill-climbing, the genetic approach leads to a more difficult control and interpretation of the path followed by the search algorithm since it is based on random operations (crossovers and mutations).

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

20201 - Electrical and electronic engineering

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2016

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

23rd Electronics, Circuits, and Systems (ICECS), 2016 IEEE International Conference on

ISBN

978-1-5090-6113-6

ISSN

—

e-ISSN

—

Number of pages

4

Pages from-to

289-292

Publisher name

IEEE

Place of publication

Monte Carlo

Event location

Monte Carlo

Event date

Nov 7, 2016

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000399230200073