1-b Observation for Direct-Learning-Based Digital Predistortion of RF Power Amplifiers
|Title:||1-b Observation for Direct-Learning-Based Digital Predistortion of RF Power Amplifiers||Authors:||Wang, Haoyu
|Permanent link:||http://hdl.handle.net/10197/8381||Date:||23-Jan-2017||Abstract:||In this paper, we propose a low-cost data acquisition approach for model extraction of digital predistortion (DPD) of RF power amplifiers. The proposed approach utilizes only 1-bit resolution analog-to-digital converters (ADCs) in the observation path to digitize the error signal between the input and output signals. The DPD coefficients are then estimated based on the direct learning architecture using the measured signs of the error signal. The proposed solution is proved to be feasible in theory and the experimental results show that the proposed algorithm achieves equivalent performance as that using the conventional method. Replacing high resolution ADCs with 1- bit comparators in the feedback path can dramatically reduce the power consumption and cost of the DPD system. The 1-bit solution also makes DPD become practically implementable in future broadband systems since it is relatively straightforward to achieve an ultra-high sampling speed in data conversion by using only simple comparators.||Funding Details:||Science Foundation Ireland||Type of material:||Journal Article||Publisher:||IEEE||Copyright (published version):||2017 IEEE||Keywords:||Analog-to-digital converter (ADC);Digital predistortion (DPD);Error signal;Linearization;Low resolution;Power amplifier (PA);Wideband||DOI:||10.1109/TMTT.2016.2642945||Language:||en||Status of Item:||Peer reviewed|
|Appears in Collections:||Electrical and Electronic Engineering Research Collection|
Show full item record
This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. For other possible restrictions on use please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.