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Adaptive Digital Predistortion Based on Hybrid Indirect Learning Structure with Variable Step-Size for Wideband HPA

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Abstract

Adaptive digital predistortion is one of the most promising linearization technique, which leads to more efficient and cost-effective high power amplifier (HPA). In this paper, a fast-converging variable step-size least mean square algorithm based on hybrid indirect learning structure is presented to provide an unbiased predistortion identification in the non-stationary signal condition for wideband HPA. By using a variable step-size strategy, the proposed identifying algorithm can adapt its learning rate according to the dynamic range of input signal. Thus, more stable learning convergence and lower steady-state mis-adjustment error are obtained simultaneously. A comprehensive theoretical analysis is presented, and some important results including learning stability, convergence behavior, and selection criteria for initialization parameter setting are derived. Both its superiority and robustness are well confirmed through extensive numerical simulations.

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Acknowledgements

The authors would like to thank the anonymous reviewers, and also thank the support by the National Natural Science Foundation of China (No. 61101145), and 111 Project (No. B08038).

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Authors and Affiliations

  1. State Key Laboratory of Integrated Service Networks, Xidian University, Xi’an, 710071, China

    Yong Wang & Fei Zhang

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  1. Yong Wang
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  2. Fei Zhang
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Correspondence to Yong Wang.

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Wang, Y., Zhang, F. Adaptive Digital Predistortion Based on Hybrid Indirect Learning Structure with Variable Step-Size for Wideband HPA. Wireless Pers Commun 96, 3015–3026 (2017). https://doi.org/10.1007/s11277-017-4338-5

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  • DOI: https://doi.org/10.1007/s11277-017-4338-5

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