2-D orthogonal polynomial model for concurrent dual-band digital predistortion based on complex Gaussian assumption

Wu, Xiaofang; Chen, Linlin

doi:10.1016/j.aeue.2021.153704

Cited by 2 publications

(1 citation statement)

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“…12, No. 4, August 2022: 3781-3791 3782 orthogonal polynomial [14], [15], which consider the memory effects along with the static nonlinearity. Among the many models that have been proposed in the literature, the memory polynomial model is a true parametric model which represents a simplified case of the Volterra model as only diagonal terms are considered and the others are zero.…”

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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Section: Introductionmentioning

confidence: 99%