volume | PIER Journals

Abstract

Traditionally, the transmitter (TX) IQ imbalances distortion and power amplifier (PA) distortion are separately modeled. In this paper, the behavior of the two distortions are unified, and characterized by a single model. Rectangular structured Focused Time-Delay Neural Network (RSFTDNN) is proposed to uniformly model IQ imbalances and PA distortions. As a result, the physical distortions in the analog circuits are further abstracted. It also saves computation resources in simulation. Unlike the polynomial based model, which suffers from condition number effects and inaccuracy for deeply nonlinear system, the proposed RSFTDNN shows high accuracy. Two cases of real experiments are carried out, where RSFTDNN model shows much better performance than the polynomial based model in the sense of model accuracy.

1. Emami, S., P. Hajireza, F. Abd-Rahman, H. Abdul-Rashid, H. Ahmad, and S. Harun, "Wide-band hybrid amplifier operating in S-band region," Progress In Electromagnetics Research, Vol. 102, 301-313, 2010.
doi:10.2528/PIER10012303 Google Scholar

2. Raab, F., P. Asbeck, S. Cripps, P. Kenington, Z. Popovic, N. Pothecary, J. Sevic, and N. Sokal, "RF and microwave power amplifier and transmitter technologies --- Part 1," High Frequency Electronics, Vol. 2, No. 3, 22-36, 2003. Google Scholar

3. Thein, T., C. Law, and K. Fu, "Frequency domain dynamic thermal analysis in GaAs Hbt for power amplifier applications," Progress In Electromagnetics Research, Vol. 118, 71-87, 2011.
doi:10.2528/PIER11050301 Google Scholar

4. Liu, T., S. Boumaiza, and F. Ghannouchi, "Deembedding static nonlinearities and accurately identifying and modeling memory effects in wide-band RF transmitters," IEEE Transactions on Microwave Theory and Techniques, Vol. 53, No. 11, 3578-3587, 2005.
doi:10.1109/TMTT.2005.857105 Google Scholar

5. Ku, H. and J. Kenney, "Behavioral modeling of nonlinear RF power amplifiers considering memory effects," IEEE Transactions on Microwave Theory and Techniques, Vol. 51, No. 12, 2495-2504, 2003.
doi:10.1109/TMTT.2003.820155 Google Scholar

6. Wulich, D., "Definition of efficient PAPR in OFDM," IEEE Communications Letters, Vol. 9, No. 9, 832-834, 2005.
doi:10.1109/LCOMM.2005.1506718 Google Scholar

7. Ding, L., Z. Ma, D. Morgan, M. Zierdt, and G. T. Zhou, "Compensation of frequency-dependent gain/phase imbalance in predistortion linearization systems," IEEE Transactions on Circuits and Systems I: Regular Papers, Vol. 55, No. 1, 390-397, 2008.
doi:10.1109/TCSI.2007.910545 Google Scholar

8. Narasimhan, B., D. Wang, S. Narayanan, H. Minn, and N. Al Dhahir, "Digital compensation of frequency-dependent joint Tx/Rx I/Q imbalance in OFDM systems under high mobility," IEEE Journal of Selected Topics in Signal Processing, Vol. 3, No. 3, 405-417, 2009.
doi:10.1109/JSTSP.2009.2020325 Google Scholar

9. Anttila, L., P. Handel, and M. Valkam, "Joint mitigation of power amplifier and I/Q modulator impairments in broadband directconversion transmitters," IEEE Transactions on Microwave Theory and Techniques, Vol. 58, No. 4, 730-739, 2010.
doi:10.1109/TMTT.2010.2041579 Google Scholar

10. Rugh, W., Nonlinear System Theory, Johns Hopkins University Press, Baltimore, MD, 1981.

11. Gharaibeh, K., O. Al-Zoubi, and A. Alzayed, "Adaptive predistortion using threshold decomposition-based piecewise linear modeling," International Journal of RF and Microwave Computer-Aided Engineering, Vol. 21, No. 2, 145-156, 2011.
doi:10.1002/mmce.20498 Google Scholar

12. Rawat, M., K. Rawat, and F. Ghannouchi, "Adaptive digital predistortion of wireless power amplifiers/transmitters using dynamic real-valued focused time-delay line neural networks," IEEE Transactions on Microwave Theory and Techniques, Vol. 58, No. 1, 95-104, 2010.
doi:10.1109/TMTT.2009.2036334 Google Scholar

13. Heath, M., Computing: An Introductory Survey, McGraw-Hill, 1998.

14. Haykin, S., Adaptive Filter Theory, Prentice-Hall, 1996.

15. Htike, K. and O. Khalifa, "Rainfall forecasting models using focused time-delay neural networks," 2010 IEEE International Conference on Computer and Communication Engineering (ICCCE), 1-6, 2010. Google Scholar

16. Doyle, F., R. Pearson, and B. Ogunnaike, Identification and Control Using Volterra Models, Springer Verlag, 2002.
doi:10.1007/978-1-4471-0107-9_1

17. Cao, H., A. Tehrani, C. Fager, T. Eriksson, and H. Zirath, "Dual-input nonlinear modeling for I/Q modulator distortion compensation," IEEE Radio and Wireless Symposium, RWS' 09, 39-42, 2009. Google Scholar

18. Kim, J. and K. Konstantinou, "Digital predistortion of wideband signals based on power amplifier model with memory," Electronics Letters, Vol. 37, No. 23, 1417-1418, 2001.
doi:10.1049/el:20010940 Google Scholar

19. Bridewell, W., N. Asadi, P. Langley, and L. Todorovski, "Reducing overfitting in process model induction," Proceedings of the 22nd International Conference on Machine Learning, ACM,, 81-88, 2005. Google Scholar

Dr. Kai Fu

Dr. Choi Look Law

Mr. Than Tun Thein