1-b Observation for Direct-Learning-Based Digital Predistortion of RF Power Amplifiers

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.