Online Levenberg-Marquardt algorithm for digital predistortion based on direct learning and indirect learning architectures

Limin Chen; Yin Liang; Guojin Wan

doi:10.1117/12.945918

14 May 2012 Online Levenberg-Marquardt algorithm for digital predistortion based on direct learning and indirect learning architectures

Limin Chen, Yin Liang, Guojin Wan

Author Affiliations +

Proceedings Volume 8334, Fourth International Conference on Digital Image Processing (ICDIP 2012); 833402 (2012) https://doi.org/10.1117/12.945918
Event: Fourth International Conference on Digital Image Processing (ICDIP 2012), 2012, Kuala Lumpur, Malaysia

Abstract

An regularization approach is introduced into the online identification of inverse model for predistortion. It is based on a modified backpropagation Levenberg-Marquardt algorithm with sliding window. Adaptive predistorter with feedback was identified respectively based on direct learning and indirect learning architectures. Length of the sliding window was discussed. Compared with the Recursive Prediction Error Method (RPEM) algorithm and Nonlinear Filtered Least-Mean-Square (NFxLMS) algorithm, the algorithm is tested by identification of infinite impulse response Wiener predistorter. It is found that the proposed algorithm is much more efficient than either of the other techniques. The values of the parameters are also smaller than those extracted by the ordinary least-squares algorithm since the proposed algorithm constrains the L2-norm of the parameters.

Citation Download Citation

Limin Chen, Yin Liang, and Guojin Wan "Online Levenberg-Marquardt algorithm for digital predistortion based on direct learning and indirect learning architectures", Proc. SPIE 8334, Fourth International Conference on Digital Image Processing (ICDIP 2012), 833402 (14 May 2012); https://doi.org/10.1117/12.945918

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