Vol. 34

Front:[PDF file] Back:[PDF file]
Latest Volume
All Volumes
All Issues
2012-11-21

Behavioral Modeling of RF Power Amplifiers with Memory Effects Using Orthonormal Hermite Polynomial Basis Neural Network

By Xiao-Hui Yuan and Quanyuan Feng
Progress In Electromagnetics Research C, Vol. 34, 239-251, 2013
doi:10.2528/PIERC12091903

Abstract

Behavioral modeling technique provides an efficient and convenient way to analyze and predict the performance of the RF power amplifiers (PAs) in system-level, and thus helps to constructe a suitable predistorter to linearize the PA system. To accurately describe the nonlinear dynamic characteristics of PAs, an orthonormal Hermite polynomial basis neural network (OHPBNN) is utilized to represent the PAs behavioral model, which outperforms, mainly in respect of modeling accuracy, the classic feedforward neural network using sigmoid activation functions. In addition, we apply an adaptive algorithm to determine the appropriate memory depth of PA behavioral model. Simulation results show that the proposed model provides more accurate prediction of the PAs output signal compared with classic neural network models.

Citation


Xiao-Hui Yuan and Quanyuan Feng, "Behavioral Modeling of RF Power Amplifiers with Memory Effects Using Orthonormal Hermite Polynomial Basis Neural Network," Progress In Electromagnetics Research C, Vol. 34, 239-251, 2013.
doi:10.2528/PIERC12091903
http://www.jpier.org/PIERC/pier.php?paper=12091903

References


    1. Ghannouchi, F. M. and O. Hammi, "Behavioral modeling and predistortion," IEEE Microwave Mag., Vol. 10, No. 7, 52-64, Dec. 2009.
    doi:10.1109/MMM.2009.934516

    2. Kim, J. and K. Konstantinou, "Digital predistortion of wideband signals based on power amplifier model with memory," Electron. Lett., Vol. 37, No. 23, 1417-1418, Nov. 2001.
    doi:10.1049/el:20010940

    3. Thein, T. T., C. L. Law, and K. Fu, "Frequency domain dynamic thermal analysis in GaAs HBT for power amplifier applications," Progress In Electromagnetics Research, Vol. 118, 71-78, 2011.
    doi:10.2528/PIER11050301

    4. Zhang, B., Y.-Z. Xiong, L. Wang, S. Hu, T.-G. Lim, Y.-Q. Zhuang, and J. L.-W. Li, "A D-band power amplifier with 30-GHz bandwidth and 4.5-dBm Psat for high-speed communicatio system," Progress In Electromagnetics Research, Vol. 107, 161-178, 2010.
    doi:10.2528/PIER10060806

    5. Zhu, A. and T. J. Brazil, "Behavioral modeling of RF power amplifiers based on pruned volterra series," IEEE Microwave and Wireless Components Lett., Vol. 14, 563-565, Dec. 2004.
    doi:10.1109/LMWC.2004.837380

    6. Saleh, A., "Frequency-independent and frequency-dependent nonlinear models of TWT amplifiers," IEEE Trans. Commun., Vol. 29, No. 11, 1715-1720, Nov. 1981.
    doi:10.1109/TCOM.1981.1094911

    7. D'Andrea, A. N., V. Lottici, and R. Reggiannini, "RF power amplifier linearization through amplitude and phase predistortion," IEEE Trans. Commun., Vol. 44, No. 11, 1477-1484, Nov. 1996.
    doi:10.1109/26.544464

    8. Cavers, J. K., "A linearizing predistorter with fast adaptation," Proc. IEEE Vehicular Techn. Conf., 41-47, May 1990.
    doi:10.1109/VETEC.1990.110293

    9. Schetzen, M., The Volterra and Wiener Thoeries Nonlinear Systems, Wiley, New York, 1980.

    10. Zhu, A., M. Wren, and T. J. Brazil, "An efficient Volterra-based behavioral model for wideband RF power amplifiers," IEEE MTT-S Int. Microwave Symp. Dig., Vol. 2, 787-790, Jun. 2003.

    11. Zhu, A. and T. J. Brazil, "Behavioral modeling of RF power amplifiers based on pruned Volterra series," IEEE Microwave and Wireless Components Lett., Vol. 14, No. 12, Dec. 2004.
    doi:10.1109/LMWC.2004.837380

    12. Morgan, D. R., Z. Ma, J. Kim, M. G. Zierdt, and J. Pastalan, "A generalized memory polynomial model for digital predistortion of RF power amplifiers," IEEE Trans. Signal Process., Vol. 54, No. 10, 3852-3860, Oct. 2006.
    doi:10.1109/TSP.2006.879264

    13. Wang, H., J. Bao, and Z. Wu, "Comparison of the behavioral modelings for RF power amplifier with memory effects," IEEE Microwave and Wireless Components Lett., Vol. 19, No. 3, 179-181, Mar. 2009.
    doi:10.1109/LMWC.2009.2013746

    14. Ding, L. and G. T. Zhou, "Effects of even-order nonlinear terms on power amplifiers modeling and predistortion linearization," IEEE Trans. Vehicular Tech., Vol. 53, 156-162, Jan. 2004.
    doi:10.1109/TVT.2003.822001

    15. Liu, T., S. Boumaiza, and F. M. Ghannouchi, "Augmented Hammerstein predistorter for linearization of broad-band wireless transmitters," IEEE Trans. Microwave Theory Tech., Vol. 54, No. 4, Apr. 2006.

    16. Sano, A. and L. Sun, "Identification of Harmmerstein-Wiener system with application to compensation for nonlinear distortion," Proc. 41st SICE Annu. Conf., Vol. 3, 1521-1526, Aug. 2002.
    doi:10.1109/SICE.2002.1196533

    17. Ibnkahla, M., J. Sombrin, F. Castanie, and N. J. Bershad, "Neural networks for modeling nonlinear memoryless communication channels," IEEE Trans. Commun., Vol. 45, 768-771, Jul. 1997.

    18. Liu, T., S. Boumaiza, and F. M. Ghannouchi, "Dynamic behavioral modeling of 3G power amplifiers using real-valued time-delay neural networks," IEEE Trans. Microwave Theory Tech., Vol. 52, No. 3, Mar. 2004.
    doi:10.1109/TMTT.2004.823583

    19. Mkadem, F. and S. Boumaiza, "Extended Hammerstein behavioral model using artificial neural networks," IEEE Trans. Microwave Theory Tech., Vol. 57, No. 4, Apr. 2009.
    doi:10.1109/TMTT.2009.2015092

    20. Cao, Y., X. Chen, and G. Wang, "Dynamic behavioral modeling of nonlinear microwave devices using real-time recurrent neural network," IEEE Trans. Electron Devices, Vol. 56, No. 5, May 2009.
    doi:10.1109/TED.2009.2016029

    21. Li, M., J. Liu, Y. Jiang, and W. Feng, "Complex-Chebyshev functional link neural network behavioral model for broadband wireless power amplifiers," IEEE Trans. Microwave Theory Tech., Vol. 60, No. 6, Jun. 2012.

    22. Naskas, N. and Y. Papananos, "Adaptive baseband predistorter for radio frequency power amplifiers based on a multiplayer perceptron," 9th Int. Electron., Circuits, Syst. Conf., Vol. 3, No. 11, 1107-1110, 2002.
    doi:10.1109/ICECS.2002.1046445

    23. Lin, F. J., et al., "Tracking control of thrust active magnetic bearing system via hermite polynomial-based recurrent neural network," IET Electric Power Applications, Vol. 4, 701-714, 2010.
    doi:10.1049/iet-epa.2010.0068

    24. Vrudhula, S., et al., "Hermite polynomial based interconnect analysis in the presence of process variations ," IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, Vol. 25, 2001-2011, 2006.
    doi:10.1109/TCAD.2005.862734