Memory aware physically enhanced polynomial model for PAs

In this study, a new behavioural model named memory aware physically enhanced polynomial model is proposed. This modelling approach is based on using a physical analysis of the power amplifier (PA) operation and takes into account the electro-thermal and electrical memory effects as main sources of long-term memory. The physical analysis-based approach allows to select the most relevant coefficients for the model and discards the less significant ones in order to minimise the complexity. In this study, the proposed methodology is demonstrated on a single-stage class-AB PA. Nevertheless, it is applicable for multi-stage PAs. The model accuracy and complexity are evaluated using measurement results from two commercial PAs. Compared to the conventional memory polynomial (MP) model, for, respectively, 20 and 80.MHz long-term evolution signals, the proposed model shows 3 and 5.dB improvement in the normalised mean square error with fewer coefficients 18 instead of 20 for a memory depth of 3. Also, with same accuracy the proposed model has considerably fewer coefficients compared with the PLUME and generalised MP (GMP) models. Applying digital predistortion (DPD), the proposed model outperforms the MP model in terms of performance and the PLUME and GMP models in terms of complexity.