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

Becerra, Juan A.; Perez-Hernandez, Abraham; Madero‐Ayora, María J.; Crespo‐Cadenas, Carlos

doi:10.1109/lmwc.2021.3079839

Cited by 4 publications

(1 citation statement)

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“…The digital pre-distortion (DPD) technique as one of the most effective methods, is typically used to overcome the non-idealities. To utilize this method, it is necessary to model the nonlinear behavior of the system with a proper mathematical model such as the memory polynomials [6]- [8], the Wiener Hammerstein model [9], Volterra series [10], [11], the optimized structures with modified algorithms [12], [13] and  ISSN: 2088-8708 Int J Elec & Comp Eng, Vol. 12, No.…”

Section: Introductionmentioning

confidence: 99%

Joint digital pre-distortion model based on Chebyshev expansion

Majdinasab

Jannesari

2022

IJECE

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<span>In this paper, a new low complexity model is proposed for the joint digital pre-distortion of in-phase/quadrature-phase (I/Q) imbalance, local oscillator (LO) leakage, and power amplifier nonlinearity in direct-conversion transmitters (DCTs). In this structure, we proposed a set of orthogonal basis functions based on Chebyshev expansion to attenuate the problem of numerical instability created during the conventional model identification method. This robust joint digital pre-distortion (DPD) utilized the indirect learning architecture and updated the coefficients vector based on the recursive least square (RLS) algorithm. To verify the operation and efficiency of the proposed model, an extensive simulation in MATLAB was carried out. The results showed a significant reduction in the conditional number and the coefficient dispersion of the observation matrix. Furthermore, the power of the signal in the adjacent channel decreased by more than 16 dB for the orthogonal frequency division multiplexing (OFDM), 16 QAM input signal. In comparison to the previous digital pre-distorter models, the proposed DPD builds strong numerical stability with the least coefficients.</span>

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