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Reconfigurable DPD based on ANNs for wideband load modulated balanced amplifiers under dynamic operation from 1.8 to 2.4 GHz

Guillena, Estefania, Li, Wantao, Montoro, Gabriel, Quaglia, Roberto and Gilabert, Pere L. 2022. Reconfigurable DPD based on ANNs for wideband load modulated balanced amplifiers under dynamic operation from 1.8 to 2.4 GHz. IEEE Transactions on Microwave Theory and Techniques 70 (1) , pp. 453-465.

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Abstract

This article proposes a methodology to ensure linear amplification of a load modulated balanced amplifier (LMBA) while keeping the power efficiency as high as possible over a frequency band ranging from 1.8 to 2.4 GHz and where the transmitted signals can present different bandwidth (BW) configurations. The proposed reconfigurable linearization methodology consists of, in a first step, tuning some free parameters (with dependence on the signal BW and frequency of operation) of the LMBA to trade-off linearity and power efficiency. In a second step, two multipurpose adaptive digital predistortion (DPD) linearizers are considered, properly combined with crest factor reduction (CFR) techniques, to meet the required linearity specifications. Either a DPD based on artificial neural networks or a DPD based on polynomials can be selected taking into account the compromise between computational complexity and linearization performance. Experimental results will validate the proposed methodology to guarantee the linearity levels (ACPR < -45 dBc and EVM < 1%) with high power efficiency in an LMBA under dynamic transmission, where both the signal BW (from 20 and up to 200-MHz instantaneous BW) and frequency of operation (in the range of 1.8-2.4 GHz) change.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Publisher: Institute of Electrical and Electronics Engineers
ISSN: 0018-9480
Date of First Compliant Deposit: 7 June 2021
Date of Acceptance: 28 May 2021
Last Modified: 21 Feb 2022 15:16
URI: https://orca.cardiff.ac.uk/id/eprint/141778

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