Direct Error-Searching SPSA-Based Model Extraction for Digital Predistortion of RF Power Amplifiers

Abstract

This paper presents a low-complexity architecture to extract model coefficients for digital predistortion of radio frequency power amplifiers. The proposed approach directly updates the model coefficients online using a stochastic optimization algorithm that utilizes random perturbation of the model coefficients to determine the coefficient updating direction and converge toward the optimum solution. This technique avoids resource-intensive matrix operations and the requirement for an offline error model in the conventional model extraction techniques and thus drastically reduces the implementation complexity. The complete model extraction solution has been implemented on a field-programmable gate array, and it is shown that the hardware resource usage is remarkably low. Experimental measurements were conducted on a gallium nitride Doherty amplifier excited by Long Term Evolution signals and the results showed that the proposed technique can achieve linearization performance comparable to that obtained by using the conventional and significantly more complex solutions.