Summary
This paper considers the problem of predistortion of IIR Hammerstein systems based on direct and indirect learning architecture methods. Two adaptive algorithms, namely: the Nonlinear Filtered-x Least Mean Squares (NFxLMS) and the Recursive Prediction Error Method (RPEM) algorithms, are developed for these two methods, respectively. The performance of the two algorithms are investigated by a comparative simulation study.
Zusammenfassung
Dieser Artikel widmet sich dem Problem der Vorverzerrung von IIR-Hammerstein-Systemen basierend auf direkten und indirekten Lernarchitekturen. Zwei adpative Algorithmen, nämlich Nonlinear Filtered-x Least Mean Squares-(NFxLMS)- und Recursive Prediction Error Method(RPEM)-Algorithmen, wurden jeweils für diese zwei Lernarchitekturen entwickelt. Die Leistungsfähigkeit der beiden Algorithmen wurde ausführlich in einer vergleichenden Simulationsstudie erforscht und verglichen.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Ding, L., Raich, R., Zhou, G. T. (2002): A Hammerstein predistortion linearization design based on the indirect learning architecture. Proc. of The IEEE Int. Conf. on Acoustics, Speech, and Signal Processing. Vol. 3, Orlando, Florida: 2689–2692
Eun, C., Powers, E. J. (1997): A new Volterra predistorter based on indirect learning architecture. IEEE Transactions on Signal Processing 45 (1): 223–227
Gao, X. Y., Snelgrove, W. M. (1990): Adaptive linearization of a loudspeaker. Proc. of the IEEE Int. Conf. on Acoustics, Speech, and Signal Processing. Vol. 5, Toronto, Canada: 3589–3592
Gilabert, P., Montoro, G., Bertran, E. (2005): On the Wiener and Hammerstein models for power amplifier predistortion. Proc. of Asia-Pacific Microwave Conf. (APMC 2005). Vol. 2, Suzhou, China
Kang, H. W., Cho, Y. S., Youn, D. H. (1998): Adaptive precompensation of Wiener systems. IEEE Transactions on Signal Processing 46 (10): 2825–2829
Lim, Y. H., Cho, Y. S., Cha, I. W., Youn, D. H. (1998): An adaptive nonlinear prefilter for compensation of distortion in nonlinear systems. IEEE Transactions on Signal Processing 46 (6): 1726–1730
Ljung, L., Söderström, T. (1983): Theory and Practice of Recursive Identification. Cambridge, MA, USA: M.I.T. Press
Nordsjö, A. E. (2002): An algorithm for adaptive predistortion of certain time-varying nonlinear hig-power amplifiers. Proc. of Int. Radar Conf. Edinburgh, UK: 469–473
Schetzen, M. (1989): The Volterra and Wiener Theories of Nonlinear Systems. Florida, USA: R. E. Krieger
Schwingshackl, D., Kubin, G. (2007): Polyphase representation of multirate nonlinear filters and its applications. IEEE Transactions on Signal Processing 55 (5): 2145–2157
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Abd-Elrady, E., Gan, L. & Kubin, G. Direct and indirect learning methods for adaptive predistortion of IIR Hammerstein systems. Elektrotech. Inftech. 125, 126–131 (2008). https://doi.org/10.1007/s00502-008-0522-3
Received:
Accepted:
Issue Date:
DOI: https://doi.org/10.1007/s00502-008-0522-3