A Fully Recurrent Neural Network-Based Model for Predicting Spectral Regrowth of 3G Handset Power Amplifiers With Memory Effects

摘要

Efficient and accurate behavioral models of power amplifiers (PAs) with memory effects are important for predicting the distortions generated by PAs in 3G handsets. Conventional recurrent neural network (RNN) has been applied for RF PAs, but its capability to model PAs with memory effects has not been investigated. In this letter, we propose a new fully RNN with Gamma tapped-delay lines suitable for modeling the dynamic behavior of 3G PAs with memory effects. After being trained with wideband code division multiple access (W-CDMA) (3GPP Uplink) signals, the proposed model is validated with not only W-CDMA but also high-speed downlink packet access (3GPP Uplink) signals with higher peak-to-average ratios (PARs), which demonstrates the generality of the model. The comparisons with previous RNN models show that the proposed model offers improved performance in predicting spectral regrowth by reducing errors by 1.7-4dB