A Complexity Reduced Non-Uniform Generalized Memory Polynomial Model for Nonlinear Power Amplifier Behavioural Modeling

Power amplifiers are widely employed electronic devices in various fields such as mobile networks and radio frequency (RF) transceivers. To achieve efficient operations, power amplifiers can often suffer from nonlinearity problems. This problem can be mitigated through the use of linearization techniques, such as digital predistortion, regarded as the most promising solution to power amplifier linearization. Behavioural modeling is a substantial part of the digital predistortion, responsible for acquiring the coefficients that are necessary to linearize the power amplifier. A Complex Reduced Non-Uniform Generalized Memory Polynomial model was proposed to reach comparable performance of accuracy as Memory Polynomial Model with reduced complexities. The proposed model was tested with a 5MHz LTE signal measured at the input and output of a Doherty PA under different conditions of nonlinearities, memory effects and attenuations as well as PA working powers. It can be observed that the proposed model shows superior accuracy at low complexities, when the PA has higher levels of nonlinearity and memory depth while still maintaining low complexities. Over 60% of coefficients reduction could be reached at the same level of accuracy compared to the MP model.