Low Computational Complexity Digital Predistortion Based on Direct Learning With Covariance Matrix

This paper proposes a novel approach for digital predistortion, based on direct learning architecture (DLA), to reduce the computational complexity. In power amplifier (PA) behavioral models, the coefficients of a Volterra polynomial or a simplified Volterra polynomial are extracted by calculating the inverse of a time-varying matrix, which is resource-consuming, time-consuming, and power-consuming due to its matrix dimension and inverse operation in a field-programmable gate array. To speed up the computation and save hardware resources, we propose a low computational complexity DLA with covariance matrix that uses the constant covariance matrix to replace the time-varying input signal filled matrix based on a stationary ergodic random process. To verify the proposed method, it was applied to a wideband Doherty gallium nitride (GaN) PA at 2.6 GHz with a 40-MHz orthogonal frequency division multiplexing signal, and to a dual-band Doherty GaN PA at 1.9 and 2.6 GHz with two 20-MHz long-term evolution signals. Experimental results show that the proposed algorithm achieves almost the same performance as the traditional approach, with less than one fifth of the computational quantity.