Skip to main content
SpringerLink
Log in

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

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Liu, Y. J., Chen, W., Zhou, J., Zhou, B., & Ghannouchi, F. (2013). Digital predistortion for concurrent dual-band transmitters using 2-D modified memory polynomials. IEEE Transactions on Microwave Theory and Techniques, 61(1), 281–290.

    Article  Google Scholar 

  2. Haykin, S. O. (2013). Adaptive filter theory (5th ed.). Upper Saddle River: Prentice Hall Press.

    MATH  Google Scholar 

  3. Mandic, D. P. (2004). A generalized normalized gradient descent algorithm. IEEE Signal Processing Letters, 11(2), 115–118.

    Article  Google Scholar 

  4. Rawat, M., Ghannouchi, F. M., & Rawat, K. (2013). Three-layered biased memory polynomial for dynamic modeling and predistortion of transmitters with memory. IEEE Transactions Circuits and Systems I: Regular Papers, 60(3), 768–777.

    Article  MathSciNet  Google Scholar 

  5. Morgan, D. R., Ma, Z., & Ding, L. (2003). Reducing measurement noise effects in digital predistortion of RF power amplifiers. In IEEE ICC (pp. 2436–2439).

  6. Ding, L., Zhou, G. T., Morgan, D. R., Ma, Z., Kenney, J. S., Kim, J., et al. (2004). A robust digital baseband predistorter constructed using memory polynomials. IEEE Transactions on Communications, 52(1), 159–165.

    Article  Google Scholar 

  7. Lee, J., Jeon, S., Kim, J., & Suh, Y. (2013). Adaptive HPA linearization technique for practical ATSC DTV system. IEEE Transactions on Broadcasting, 59(2), 376–381.

    Article  Google Scholar 

  8. Ding, L., Ma, Z., Morgan, D. R., Zierdt, M., & Pastalan, J. (2006). A least-squares/Newton method for digital predistortion of wideband signals. IEEE Transactions on Communications, 54(5), 833–840.

    Article  Google Scholar 

  9. Zhou, D., & DeBrunner, V. E. (2007). Novel adaptive nonlinear predistorters based on the direct learning algorithm. IEEE Transactions on Signal Processing, 55(1), 120–133.

    Article  MathSciNet  Google Scholar 

  10. Liu, Y. J., Zhou, J., Chen, W., & Zhou, B. (2014). A robust augmented complexity -reduced generalized memory polynomial for wideband RF power amplifiers. IEEE Transactions on Industrial Electronics, 61(5), 2389–2401.

    Article  Google Scholar 

  11. Kim, J., & Konstantinou, K. (2001). Digital predistortion of wideband signals based on power amplifier model with memory. Electronics Letters, 37, 1417–1418.

    Article  Google Scholar 

  12. Liu, Y. J., Chen, W., Zhou, J., Zhou, B. H., Ghannouchi, F. M., & Liu, Y. N. (2012). Modified least squares extraction for Volterra-series digital predistorter in the presence of feedback measurement errors. IEEE Transactions on Microwave Theory and Techniques, 60(11), 3559–3570.

    Article  Google Scholar 

  13. Slock, D. T. M., & Kailath, T. (1988). Numerically stable fast recursive least squares transversal filters. In ICASSP, (pp. 1365–1368).

Download references

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).

Author information

Authors and Affiliations

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

    Yong Wang & Fei Zhang

Authors
  1. Yong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yong Wang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Published:

  • Issue Date:

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

Keywords

Search

Navigation