Digital Predistortion With Advance/Delay Neural Network and Comparison With Volterra Derived Models

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F14%3APU110575" target="_blank" >RIV/00216305:26220/14:PU110575 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Digital Predistortion With Advance/Delay Neural Network and Comparison With Volterra Derived Models

Popis výsledku v původním jazyce

This paper is focused on digital predistortion using neural networks (NNETs) for linearization of power amplifiers. We propose a new architecture of NNET. It is based on a feedforward tapped delay line neural network for complex signals with one hidden layer but it includes both delayed and advanced samples at its input. We name this architecture TADNN (tapped advance and delay line neural net). We show that the introduction of advance taps improves the digital predistortion (DPD) performance. We also compare the TADNN predistorter with predistorters derived from Volterra series such as memory polynomial or dynamic deviation reduction models. This comparison is based on three elements: performance in linearization, complexity, increase of the peak to average power ratio (PAPR) by the predistorter. Indeed, one drawback of Volterra based predistorters is that they can generate predistorted signals with very high PAPR that cannot be applied directly at the input of the power amplifier. Co

Název v anglickém jazyce

Digital Predistortion With Advance/Delay Neural Network and Comparison With Volterra Derived Models

Popis výsledku anglicky

This paper is focused on digital predistortion using neural networks (NNETs) for linearization of power amplifiers. We propose a new architecture of NNET. It is based on a feedforward tapped delay line neural network for complex signals with one hidden layer but it includes both delayed and advanced samples at its input. We name this architecture TADNN (tapped advance and delay line neural net). We show that the introduction of advance taps improves the digital predistortion (DPD) performance. We also compare the TADNN predistorter with predistorters derived from Volterra series such as memory polynomial or dynamic deviation reduction models. This comparison is based on three elements: performance in linearization, complexity, increase of the peak to average power ratio (PAPR) by the predistorter. Indeed, one drawback of Volterra based predistorters is that they can generate predistorted signals with very high PAPR that cannot be applied directly at the input of the power amplifier. Co

Druh

D - Stať ve sborníku

CEP obor

JA - Elektronika a optoelektronika, elektrotechnika

OECD FORD obor

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Projekt

<a href="/cs/project/ED2.1.00%2F03.0072" target="_blank" >ED2.1.00/03.0072: Centrum senzorických, informačních a komunikačních systémů (SIX)</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2014

Kód důvěrnosti údajů

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

Název statě ve sborníku

IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC

ISBN

978-1-4673-6235-1

ISSN

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e-ISSN

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Počet stran výsledku

5

Strana od-do

1-5

Název nakladatele

Neuveden

Místo vydání

Washington D.C., USA

Místo konání akce

Washington D.C.

Datum konání akce

2. 9. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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