A Reduced-Complexity Direct Learning Architecture for Digital Predistortion Through Iterative Pseudoinverse Calculation

• Published in: IEEE microwave and wireless components letters 2021-08, Vol.31 (8), p.933-936
• Main Authors: Becerra, J. A., Perez-Hernandez, A., Madero-Ayora, M. J., Crespo-Cadenas, C.
• Format: Article
• Language: English
• Subjects: Algorithms; Complexity; Computational complexity; Computer architecture; Convergence; Covariance matrix; Digital predistortion (DPD); direct learning; Iterative methods; Learning; Microwave theory and techniques; Polynomials; power amplifier (PA); Power amplifiers; Predistortion; Proposals; Volterra series; Wireless communication
• ISSN: 1531-1309; 2771-957X; 1558-1764; 2771-9588
• DOI: 10.1109/LMWC.2021.3079839

In this letter, a novel approach is proposed for digital predistortion (DPD) with direct learning architecture (DLA). Regression of a Volterra behavioral model requires the pseudoinverse of a matrix, which needs many resources due to the inverse operation when the Moore-Penrose pseudoinverse is used. This work substitutes the pseudoinverse calculation by a polynomial expansion (PE) method to obtain a polynomial expansion direct learning architecture (PE-DLA), which attains a pseudoinverse in an iterative fashion avoiding the inverse operation and consequently reducing the algorithm computational complexity. Experimental results show that the number of iterations in the PE-DLA affects the convergence speed. The proposal is benchmarked against other state-of-the-art approaches such as the classic DLA and the covariance matrix DLA (CM-DLA) in the DPD of a commercial class AB power amplifier, concluding that the linearization performance of the current proposal is equivalent to others while featuring simple operations.