Complex-Chebyshev Functional Link Neural Network Behavioral Model for Broadband Wireless Power Amplifiers

The Neural Network (NN) based models are commonly used in power amplifier modeling and predistorter design, and seen as a potential alternative to model and compensate broadband power amplifiers (PAs) having medium- to-strong memory effects along with high-order nonlinearity. In this paper, we propose a novel computationally efficient behavior model based on complex-Chebyshev functional link neural network (CCFLNN) suitable for dynamic modeling of wireless PAs. The CCFLNN exhibits a simpler compact structure than the previously reported NNs and can require less computational burden during the learning process since it uses the complex-valued topology and does not need the hidden layers, which exist in most of the conventional neural-network-based models. The proposed approach utilizes the complex-valued inverse QR-decomposition-based recursive least square algorithm to update the weighting coefficients of the CCFLNN model. The proposed model is comparatively compared with a real-valued focused time-delay NN model and a conventional memory polynomial model with respect to computation complexities and modeling performance. The accurate modeling capacity of the CCFLNN model is demonstrated through a full characteristic (working in the strongly nonlinear region) 170-W class AB amplifier driven by a multicarrier WCDMA signal. Furthermore, the proposed model has been applied for linearizing a real PA in multicarrier application. Results obtained from the measurement clearly show that the proposed digital predistorter can eliminate various intensity in-band and out-of band distortions.