Behavioral modeling of nonlinear RF power amplifiers using ensemble SDBCC network
A new behavioral model of nonlinear RF power amplifiers (PA) is presented to treat memory effects. The key issue here is considering the phenomenon that uplink quadrature modulation signals are influenced not only by the current uplink signals, but also by previous terms. The variation of AM/AM and AM/PM, and the asymmetries in lower and upper intermodulation terms are frequently observed in high-power PAs. To treat these phenomena, this paper proposes a model based on artificial neural network. The contribution made of this model is to solve the order determination issue (determining the order of the previous output and input signals that have influence on the current output signal). In addition, the recognized difficulty of long training process is overcome by using SDBCC algorithm, a novel neural network design method combining structure decomposition and the Cascade-Correlation neural network algorithm. The required maximum delay is established by examining the autocorrelation coefficient of the residual error. Ensemble system is finally used to improve the performance further. This proposed method is successfully validated in nonlinear modeling of the RF PAs from HuaWei Company, including 8000 samples.
rf power amplifier memory effects order determination cascade-correlation algorithm parallel learning