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Low-complexity, Multi Sub-band Digital Predistortion

Novel Algorithms and SDR Verification

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Abstract

The nonlinearities of power amplifiers combined with non-contiguous transmissions found in modern, frequency-agile, wireless standards create undesirable spurious emissions through the nearby spectrum of data carriers. Digital predistortion (DPD) is an effective way of combating spurious emission violations without the need for a significant power reduction in the transmitter leading to better power efficiency and network coverage. In this paper, an iterative, multi sub-band version of the sub-band DPD, proposed earlier by the authors, is presented. The DPD learning is iterated over intermodulation distortion (IMD) sub-bands until a satisfactory performance is achieved for each of them. A sequential DPD learning procedure is also presented to reduce the hardware complexity when higher order nonlinearities are incorporated in the DPD learning. Improvements in the convergence speed of the adaptive DPD learning are also achieved via incorporating a variable learning rate and interpolation of previously trained DPD coefficients. A WarpLab implementation of the proposed DPD is also shown with excellent suppression of the targeted spurious emissions.

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Acknowledgments

This work was supported by the Finnish Funding Agency for Technology and Innovation (Tekes) under the project “Future Small-Cell Networks using Reconfigurable Antennas (FUNERA).” This work was also supported in part by the US National Science Foundation under grants ECCS-1408370, ECCS-1232274, CNS-1265332, and CNS-1717218 in the WiFiUS program. The work was also funded by the Academy of Finland under the projects 288670 “Massive MIMO: Advanced Antennas, Systems and Signal Processing at mm-Waves,” 284694 “Fundamentals of Ultra Dense 5G Networks with Application to Machine Type Communication,” and 301820 “Competitive Funding to Strengthen University Research Proles,” and by the Linz Center of Mechatronics (LCM) in the framework of the Austrian COMET-K2 program.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Rice University, Houston, TX, 77005, USA

    Chance Tarver & Joseph R. Cavallaro

  2. Department of Electronics and Communication Engineering, Tampere University of Technology, Tampere, Finland

    Mahmoud Abdelaziz, Lauri Anttila & Mikko Valkama

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  1. Chance Tarver
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  2. Mahmoud Abdelaziz
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  3. Lauri Anttila
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  4. Mikko Valkama
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  5. Joseph R. Cavallaro
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Correspondence to Chance Tarver.

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Tarver, C., Abdelaziz, M., Anttila, L. et al. Low-complexity, Multi Sub-band Digital Predistortion. J Sign Process Syst 90, 1495–1505 (2018). https://doi.org/10.1007/s11265-017-1303-1

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  • DOI: https://doi.org/10.1007/s11265-017-1303-1

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