Abstract
This paper shows and compares three techniques based on the least squared error for the estimation of the constant coefficients of the memory polynomial model used for the modeling of power amplifiers for radio-frequency and for the construction of a pre-distorter. The first technique is the conventional linear regression using the least square error method. The second technique is the order recursive least squares which can be used for exploring the most adequate nonlinearity order and memory depth of the memory polynomial model by comparing subsequent errors. The sequential least squares method is useful when the measurements of a system are coming sample by sample and the parameters of the model should be adjusted on-line. The mathematical background of the three methods is shown; as an experimental validation of this methods they were simulated in Matlab for the measurements of a 10W NPX Power Amplifier based on the transistor CLF1G0060 GaN HEMTs. An NMSE of \(-19.83\) dB was reached for the best model. Also in order to linearize the power amplifier a pre-distorter was constructed through indirect learning architecture achieving a 50 dBm spurious free dynamic range and a 25 dBc reduction in the adjacent power ratio.
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Acknowledgements
The authors wish to thank PhD. Patrick Roblin, Professor at Ohio State University, for its support provided through the measuring data. In addition, the authors would like to express their gratitude to the IPN for its financial support by the project SIP-20170588.
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Allende-Chávez, E. et al. (2018). Coefficients Estimation of MPM Through LSE, ORLS and SLS for RF-PA Modeling and DPD. In: Maldonado, Y., Trujillo, L., Schütze, O., Riccardi, A., Vasile, M. (eds) NEO 2016. Studies in Computational Intelligence, vol 731. Springer, Cham. https://doi.org/10.1007/978-3-319-64063-1_10
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