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

Abstract

Two-dimensional (2-D) polynomial models are widely used in digital predistortion (DPD) design for dual-band power amplifier (PA) linearization. However, the conventional polynomial model exhibits numerical instabilities when high-order nonlinearities are included. To solve this problem, a closed-form 2-D orthogonal polynomial DPD model is deduced under the assumption of complex Gaussian processes. Compared with the Legendre orthogonal polynomial for uniform distribution, the complex Gaussian assumption is more coincident with practical communication signals. Cutting down the condition number of the input correlation matrix is the most important objective to deduce the new model, and the experiment results show that the condition number of the new model can be significantly decreased both for the Gaussian signal and the practical OFDM signal. The predistortion performance of the OFDM signal is also investigated. In the presence of 32-bit floating point single precision processing, the proposed model is more stable in the least squares (LS) parameter estimation and yield better adjacent channel power ratio (ACPR) and normalized mean square error (NMSE) improvement when compared with the conventional model and the Legendre model. At the same time, the proposed model has fewer polynomial coefficients compared with the Legendre model and accordingly reduce the complexity of coefficient estimation.