Abstract
This work presents a strategy to upgrade models for power amplifier (PA) behavioral modeling and digital predistortion (DPD). These incomplete structures are the consequence of nonlinear order and memory depth model truncation with the purpose of reducing the demand of the limited computational resources available in standard processors. On the other hand, the alternative use of model structures pruned a priori does not guarantee that every significant term is included. To improve the limited performance of an incomplete model, a general procedure to augment its structure by incorporating significant terms is demonstrated. The sparse nature of the problem allows a successive search incorporating additional terms with higher nonlinear order and memory depth. This approach is investigated in the modeling and linearization of a commercial class AB PA operating at a compression point of about 6 dB, and a class J PA operating near saturation. Results highlight the capabilities of this upgrading procedure in the improvement of linearization capabilities of DPDs.
Highlights
Departamento de Teoría de la Señal y Comunicaciones, Escuela Técnica Superior de Ingeniería, Abstract: This work presents a strategy to upgrade models for power amplifier (PA) behavioral modeling and digital predistortion (DPD)
The set of active regressors identified for some conventional models (MP, generalized memory polynomial (GMP), etc.) can be insufficient to produce an optimal sparse model
Sometimes, it is almost impossible to cope with the massive set of full Volterra (FV) regressors
Summary
The complex envelope at the output of a PA can be described with a discrete-time version of the baseband Volterra model advanced in [11]. It is possible to search within the whole FV-regressors set those active regressors that guarantee the FV performance. As for a given nonlinear order and memory length, X contains the complete set of regressors of the FV model (1), here and below we refer to X as the whole FV-regressors matrix. If the procedure is applied to the GMP, or any other a priori pruned model, we cannot affirm that the identified set is complete because the richness of the initial set of regressors may be insufficient and there is no guarantee that the selected set of active regressors achieves the best performance. The procedure proposed in this paper is to upgrade the incomplete model starting with the attachment of new stock of FV normalized regressors with higher nonlinear order and/or memory depth. Three cases of study are analyzed to motivate the upgrading procedure
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