Structure-Adaptive Modeling Framework for Digital Predistortion of Concurrent Multiband RF Wireless Transmitters

Accurate modeling and measurement of nonlinear distortions in RF transmitters are essential for reliable signal transmission in 5G systems operating under concurrent multi-band (MB) scenarios. This paper proposes a basis function coding-based adaptive piecewise (BCAP) modeling framework for MB digital predistortion (DPD), named MB-BCAP, which is built upon the measured response of RF transmitters under concurrent MB excitation. The basis function coding is proposed by modifying the coding method of genetic algorithm (GA) to represent a single basis function as a gene chain that includes a specific basis-parameter vector. This approach transforms the basis function selection process into an optimal parameter combination search problem, achieving structural self-optimization during evolution by GA. Using crossover, mutation, specific fitness evaluations, and piecewise modeling, the MB-BCAP model achieves adaptive modeling in each iteration, effectively balancing model performance and running complexity. To validate the effectiveness of the proposed model, experiments are conducted on a high-efficiency power amplifier (PA) based on InGaP/GaAs HBT technology. Experimental results show that the proposed MB-BCAP model not only achieves a favorable trade-off between performance and complexity, but also demonstrates strong robustness and adaptability under various carrier configurations, making it a promising solution for future 5G and beyond transmitter linearization.