Feed-forward artificial neural networks (ANNs) can provide the adequate model required for the linearization of power amplifiers (PAs) used in wireless communication systems. A common characteristic of previously available ANN-based models for linearization purposes is the use of a single real-valued ANN having two outputs. The contribution of this work is to report the benefits of performing such behavioral modeling based on two independent real-valued ANNs, where each network has a unique output. The proposed ANN-based model is applied to the behavioral modeling of a GaN HEMT class AB PA, and its accuracy is compared to previous approaches in two different scenarios. First, in case of similar number of network parameters, it is observed that the proposed ANN-based model can reduce the normalized mean-square error (NMSE) by up to 1.3 dB. Second, in a situation of comparable modeling accuracy (NMSE = -40 dB), it is observed that the proposed ANN-based model can reduce the number of network parameters by up to 40% (from 62 to 38 real-valued parameters).
1. Cripps, S., RF Power Amplifiers for Wireless Communications, Artech House, Norwood, 2006.
2. Raab, H., P. Asbeck, S. Cripps, P. B. Kenington, Z. B. Popovic, N. Pothecary, J. F. Sevic, and N. O. Sokal, "Power amplifiers and transmitters for RF and microwave," IEEE Trans. Microw. Theory Tech., Vol. 50, No. 3, 814-826, 2002. doi:10.1109/22.989965
3. Raychaudhuri, D. and N. B. Mandayam, "Frontiers of wireless and mobile communications," Proc. IEEE, Vol. 100, No. 4, 824-840, 2012. doi:10.1109/JPROC.2011.2182095
4. Piazzon, L., R. Giofre, P. Colantonio, and F. Giannini, "A method for designing broadband Doherty power amplifiers," Progress In Electromagnetics Research, Vol. 145, 319-331, 2014. doi:10.2528/PIER14011301
5. Kenington, P. B., High Linearity RF Amplifier Design, Artech House, Norwood, 2000.
6. Pedro, J. C. and S. A. Maas, "A comparative overview of microwave and wireless power-amplifier behavioral modeling approaches," IEEE Trans. Microw. Theory Tech., Vol. 53, No. 4, 1150-1163, 2005. doi:10.1109/TMTT.2005.845723
7. Schetzen, M., "Nonlinear system modeling based on the Wiener theory," Proc. IEEE, Vol. 69, No. 12, 1557-1573, 1981. doi:10.1109/PROC.1981.12201
8. Wang, H., H. Ma, and J. Chen, "A multi-status behavioral model for the elimination of electrothermal memory effect in DPD system," Progress In Electromagnetics Research C, Vol. 47, 103-109, 2014. doi:10.2528/PIERC13112803
9. Sun, G., C. Yu, Y. Liu, S. Li, and J. Li, "An accurate complexity-reduced simplified volterra series for RF power amplifiers," Progress In Electromagnetics Research C, Vol. 47, 157-166, 2014. doi:10.2528/PIERC13121201
10. Haykin, S., Neural Networks: A Comprehensive Foundation, Prentice Hall, New Jersey, 1999.
11. Liu, T., S. Boumaiza, and F. M. Ghannouchi, "Dynamic behavioral modeling of 3G power amplifiers using real-valued time-delay neural networks," IEEE Trans. Microw. Theory Tech., Vol. 52, No. 3, 1025-1033, 2004. doi:10.1109/TMTT.2004.823583
12. Yuan, X.-H. and Q. 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
13. Fu, K., C. L. Law, and T. T. Thein, "Novel neural network model of power amplifier plus IQ imbalances," Progress In Electromagnetics Research B, Vol. 46, 177-192, 2013. doi:10.2528/PIERB12082404
14. Isaksson, M., D. Wisell, and D. Ronnow, "Wide-band dynamic modeling of power amplifiers using radial-basis function neural networks," IEEE Trans. Microw. Theory Tech., Vol. 53, No. 11, 3422-3428, 2005. doi:10.1109/TMTT.2005.855742
15. Lima, E. G., T. R. Cunha, and J. C. Pedro, "A physically meaningful neural network behavioral model for wireless transmitters exhibiting PM-AM/PM-PM distortions," IEEE Trans. Microw. Theory Tech., Vol. 59, No. 12, 3512-3521, 2011. doi:10.1109/TMTT.2011.2171709
16. Jeruchim, M. C., P. Balaban, and K. S. Shanmugan, Simulation of Communication Systems --- Modeling, Methodology, and Techniques, Kluwer Academic/Plenum Publishers, New York, 2000.
17. Vuolevi, J. H. K., T. Rahkonen, and J. P. A. Manninen, "Measurement technique for characterizing memory effects in RF power amplifiers," IEEE Trans. Microw. Theory Tech., Vol. 49, No. 8, 1383-1389, 2001. doi:10.1109/22.939917
18. Bosch, W. and G. Gatti, "Measurement and simulation of memory effects in predistortion linearizers," IEEE Trans. Microw. Theory Tech., Vol. 37, No. 12, 1885-1890, 1989. doi:10.1109/22.44098
19. Benedetto, S., E. Biglieri, and R. Daffara, "Modeling and performance evaluation of nonlinear satellite links --- A Volterra series approach," IEEE Trans. Aerosp. Electron. Syst., Vol. 15, No. 4, 494-507, 1979. doi:10.1109/TAES.1979.308734
20. Chen, S., C. F. N. Cowan, and P. M. Grant, "Orthogonal least squares learning algorithm for radial basis function networks," IEEE Trans. Neural Netw., Vol. 2, No. 2, 302-309, 1991. doi:10.1109/72.80341
21. Isaksson, M., D. Wisell, and D. Ronnow, "A comparative analysis of behavioral models for RF power amplifiers," IEEE Trans. Microw. Theory Tech., Vol. 54, No. 1, 348-359, 2006. doi:10.1109/TMTT.2005.860500