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In this paper, an augmented crossover memory polynomial model (A-COMPM) is proposed and developed which can be used for characterizing and linearizing multiple-input multiple-output (MIMO) transmitters in the presence of linear and nonlinear crosstalk. The proposed model significantly improves the performance of the crossover memory polynomial model (CO-MPM) by more accurately incorporating the effect of crosstalk. The proposed model performs comparably to the $2\times 2$ parallel Hammerstein ($2\times 2$ PH) model, while requiring the same number of coefficients as CO-MPM and a lower number of coefficients than the $2\times 2$ PH. The model was tested for forward modeling and digital predistortion (DPD) applications in the presence of both linear and nonlinear crosstalk. Experimental results show that the model outperforms the CO-MPM and $2\times 2$ PH DPDs, at a lower number of coefficients compared to both models. Furthermore, the issue of numerical stability of the DPD extraction and implementation procedures is addressed in the model. [ABSTRACT FROM PUBLISHER]

This paper presents a method for pruning power amplifier (PA) behavioral models and digital predistorters based on compressed sampling theory. Using this method, the number of coefficients required by behavioral models and predistorters can be significantly reduced while achieving comparable performance in terms of both modeling accuracy and suppressing distortions. Hardware measurements obtained for a Doherty PA driven by a five-carrier 100-MHz wide long-term evolution-advanced signal demonstrate the capability of the proposed method to linearize a highly nonlinear PA prototype using a minimal number of coefficients, revealing the attractive properties of the proposed method and its desirable performance. Using the proposed technique, predistorters achieving similar linearization performance while requiring significantly less coefficients than the traditional models were demonstrated. [ABSTRACT FROM AUTHOR]