Hostname: page-component-77c89778f8-9q27g Total loading time: 0 Render date: 2024-07-22T10:21:31.957Z Has data issue: false hasContentIssue false

Application of embedding dimension estimation to Volterra series-based behavioral modeling and predistortion of wideband RF power amplifier

Published online by Cambridge University Press:  07 February 2013

Bilel Fehri
Affiliation:
EMRG Research Group, Department of Electrical and Computer Engineering, University of Waterloo, 200 University Ave W., Waterloo, ON, CanadaN2L-3G1
Slim Boumaiza*
Affiliation:
EMRG Research Group, Department of Electrical and Computer Engineering, University of Waterloo, 200 University Ave W., Waterloo, ON, CanadaN2L-3G1
*
Corresponding author: Slim Boumaiza Email: sboumaiz@ecemail.uwaterloo.ca

Abstract

This paper expounds the systematic modeling of the behavior of radio frequency (RF) power amplifiers (PAs) exhibiting nonlinear, dynamic behavior. The approach begins with an analysis of the PA output signal to deduce the minimum embedding parameters required to accurately model its response, particularly the nonlinearity order and memory effects depth. The knowledge of the RF PA is then exploited in limiting the number of kernels consequently addressing the complexity of the Volterra series which has been the key hindrance to its wider practical adoption. In the proposed Volterra series model, performance is assessed and compared to memory polynomial model and dynamic deviation reduction Volterra models when used to linearize different high-power amplifiers driven with wideband signals of bandwidth up to 40 MHz. Significant linearization performance is achieved using a reduced number of kernels.

Type
Research Papers
Copyright
Copyright © Cambridge University Press and the European Microwave Association 2013

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

[1]Pedro, J.C.; Maas, S.A.: A comparative overview of microwave and wireless power-amplifier behavioral modeling approaches. IEEE Trans. Microw. Theory Tech., 53 (2005), 11501163.Google Scholar
[2]Isaksson, M.; Wissel, D.; Rönnow, D.: A comparative analysis of behavioral models for rf power amplifiers. IEEE Trans. Microw. Theory Tech., 54 (1) (2006), 348359.CrossRefGoogle Scholar
[3]Ku, H.; Kenny, S.: Behavioral modeling of nonlinear RF power amplifiers considering memory effects. IEEE Trans. Microw. Theory Tech., 51 (12) (2003), 24952504.Google Scholar
[4]Mkadem, F.; Boumaiza, S.: Physically inspired neural network model for RF power amplifier behavioral modeling and digital predistortion. IEEE Trans. Microw. Theory Tech., 59 (4) (2011), 913923.Google Scholar
[5]Forsythe, G.E.: Generation and use of orthogonal polynomials for data-fitting with a digital computer. J. Soc. Ind. Appl. Math., 5 (1957), 7488.Google Scholar
[6]Raich, R.; Zhu, G.T.: Orthogonal polynomials complex gaussian process. IEEE Trans. Signal Process., 52 (2004), 27882797.Google Scholar
[7]Ohmori, S.; Guangsheng, X.; Muta, O.; Akaiwa, Y.: An adaptive predistortion method based on orthogonal polynomial expansion for nonlinear distortion compensation. The 18th Annual IEEE Int. Symp. on Personal, Indoor and Mobile Radio Communications. PIMRC ‘07, September 2007.Google Scholar
[8]Cunha, T.R.; Pedro, J.C.; Lima, E.G.: Low-pass equivalent feedback topology for power amplifier modeling, 2008 IEEE MTT-S Int. Microwave Symp. Digest, pp. 14451448, June 2008.Google Scholar
[9]Casdagli, M.: A dynamical systems approach to modeling input–output systems. Nonlinear Modeling and Forecasting, SFI Studies in the Sciences of Complexity, Proc., 1992, Vol. XII.Google Scholar
[10]Wood, J.; Lefevre, M.; Runton, D.; Nanan, J.-C.; Noori, B.H.; Aaen, P.H.: Envelope-domain time series (ET) behavioral model of a doherty RF power amplifier for system design. IEEE Trans. Microw. Theory Tech., 54 (2006), 31633172.Google Scholar
[11]Schreurs, D.; Wood, J.; Tufillaro, N.; Usikov, D.; Barford, L.; Root, D.E.: Construction of behavioral models for microwave devices from time domain large-signal measurements to speed up high-level design simulation, Int. J. RF Microw. Comput.-Aided Eng., 13 (2003), 5461.Google Scholar
[12]Cao, L.; Mees, A.; Judd, K.; Froyland, G.: Determining the minimum embedding dimension of input–output time series data. Int. J. Bifurcation Chaos, 8 (3) (1998), 14901504.Google Scholar
[13]Abarbanel, H.D.I.: Analysis of Observed Chaotic Data, Springer, New York, 1996.CrossRefGoogle Scholar
[14]Zhu, A.; Pedro, J.C.; Brazil, T.J.: Dynamic deviation reduction-based volterra behavioral modeling of RF power amplifiers. IEEE Trans. Microw. Theory Tech., 54 (12) (2006), 43234332.Google Scholar