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Evolutionary multiobjective optimization for digital predistortion architectures

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
OtsikkoCognitive Radio Oriented Wireless Networks
Alaotsikko11th International Conference, CROWNCOM 2016, Grenoble, France, May 30 - June 1, 2016, Proceedings
KustantajaSpringer Verlag
Sivut498-510
Sivumäärä13
ISBN (painettu)9783319403519
DOI - pysyväislinkit
TilaJulkaistu - 2016
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaInternational Conference on Cognitive Radio Oriented Wireless Networks -
Kesto: 1 tammikuuta 1900 → …

Julkaisusarja

NimiLecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
Vuosikerta172
ISSN (painettu)1867-8211

Conference

ConferenceInternational Conference on Cognitive Radio Oriented Wireless Networks
Ajanjakso1/01/00 → …

Tiivistelmä

In wireless communication systems, high-power transmitters suffer from nonlinearities due to power amplifier (PA) characteristics, I/Q imbalance, and local oscillator (LO) leakage. Digital Predistortion (DPD) is an effective technique to counteract these impairments. To help maximize agility in cognitive radio systems, it is important to investigate dynamically reconfigurable DPD systems that are adaptive to changes in the employed modulation schemes and operational constraints. To help maximize effectiveness, such reconfiguration should be performed based on multidimensional operational criteria. With this motivation, we develop in this paper a novel evolutionary algorithm framework for multiobjective optimization of DPD systems. We demonstrate our framework by applying it to develop an adaptive DPD architecture, called the adaptive, dataflow-based DPD architecture (ADDA), where Pareto-optimized DPD parameters are derived subject to multidimensional constraints to support efficient predistortion across time-varying operational requirements and modulation schemes. Through extensive simulation results, we demonstrate the effectiveness of our proposed multiobjective optimization framework in deriving efficient DPD configurations for run-time adaptation.

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