volume | PIER Journals

Abstract

In this paper, a behavioral model with multi-status is represented to describe electrothermal memory of power amplifiers. In the model, a multi-point linearity approximation method is introduced to estimated the model coefficients at random status. The parameters of the new model can be identified by traditional identification algorithms such as recursive least square (RLS) with the input and feedback data at predetermined status points captured by the digital predistortion (DPD) system. Compared with traditional models, the coefficients estimation speed for the new model at new status is very fast. The final experiment result proves that the multi-status model can efficiently ameliorate the performance deterioration caused by electrothermal memory in the the DPD system, and at the same time it still keeps very high DPD performance.

1. Pedro, J. C. and S. A. Maas, "A comparative overview of microwave of microwave and wireless power-amplifier behavioral modeling approaches," IEEE Trans. on Microw. Theory and Tech., Vol. 53, No. 4, 1150-1163, 2005.
doi:10.1109/TMTT.2005.845723 Google Scholar

2. Isaksson, M., D. Wisell, and D. Ronnow, "A comparative analysis of behavioral models for RF power amplifiers," IEEE Trans. on Microw. Theory and Tech., Vol. 54, No. 1, 348-358, 2006.
doi:10.1109/TMTT.2005.860500 Google Scholar

3. 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 Google Scholar

4. Du, T., C. Yu, J. Gao, Y. Liu, S. Li, and Y. Wu, "A new accurate volterra-based model for behavioral modeling and digital predistortion of RF power amplifiers," Progress In Electromagnetics Research C, Vol. 29, 205-218, 2012.
doi:10.2528/PIERC12032707 Google Scholar

5. Hammi, O., M. Younes, A. Kwan, M. Smith, and F. M. Ghannouchi, "Performance-driven dimension estimation of memory polynomial behavioral models for wireless transmitters and power amplifiers," Progress In Electromagnetics Research C, Vol. 12, 173-189, 2010.
doi:10.2528/PIERC10012611 Google Scholar

6. Morgan, D., Z. Ma, and J. Kim, "A generalized memory polynomial model for digital predistortion of RF power amplifiers," IEEE Trans. on Signal Process., Vol. 54, No. 10, 3852-3860, 2006.
doi:10.1109/TSP.2006.879264 Google Scholar

7. Robin, P., D. E. Root, J. Verspecht, Y. Ko, and J.Teyssoer, "New trends for the nonlinear measurement and modeling of high-power RF transistors and amplifiers with memory effects," IEEE Trans. on Microw. Theory and Tech., Vol. 60, No. 10, 3852-3860, 2012. Google Scholar

8. Pedro, J. C., P. M. Cabral, T. R. Cunha, and P. Lavrador, "A multiple time-scale power amplifier behavioral model for linearity and e±ciency calculations," IEEE Trans. on Microw. Theory and Tech., Vol. 61, No. 1, 606-615, 2013.
doi:10.1109/TMTT.2012.2227779 Google Scholar

9. Tehrani, A. S., T. Eriksson, and C. Fager, "Modeling of long term memory effects in RF power amplifiers with dynamic parameters," IEEE MTT-S Int. Microwave Symp. Dig., 1-3, Montreal, Canada, June 2012. Google Scholar

10. Cadenas, C. C., J. Tosina, and M. Madero, "Study of a power amplifier behavioral model with nonlinear thermal effects," Proceedings of the 40th European Microwave Conference, 1138-1141, Paris, France, September 2010. Google Scholar

11. Ding, L, G. T. Zhou, D. R. Morgan, Z. Ma, and J. S. Kenney, "A robust digital baseband predistorter constructed using memory polynomial," IEEE Trans. on Commun., Vol. 52, No. 1, 159-165, 2004.
doi:10.1109/TCOMM.2003.822188 Google Scholar

Dr. Huadong Wang

Dr. Hong Ma

Dr. Jinfeng Chen