Block-Oriented Recurrent Neural Network for Digital Predistortion of RF Power Amplifiers

In this article, a novel block-oriented recurrent neural network (RNN) model is proposed for behavioral modeling and digital predistortion (DPD) of radio frequency (RF) power amplifiers (PAs). This article provides an insightful discussion on the importance of input-end parallel finite impulse response (FIR) filters for performance enhancement and finds, for the first time, the unique linearization correction effect of each FIR filter in input-end parallel FIR filters at different frequencies, which is also the reason why block-oriented time-delay NN (BOTDNN) outperforms vector decomposition-based time-delay NN (VDTDNN) in terms of linearization performance. In order to retain the interaction information between nonlinearity and memory effects, the proposed model preserves the feedback path in feedback PA behavioral model. With a view to addressing the challenge of parameter extraction caused by the feedback structure, this article first demonstrates the potential relationship between RNN cells and feedback structures. Subsequently, considering the trade-off between complexity and performance, the Just Another NETwork (JANET) cell is chosen to construct the feedback structure to form the proposed block-oriented JANET (BO-JANET) model. The BO-JANET model is validated using two PAs with the center frequencies of 2.4 and 3.55 GHz, respectively. Experimental results demonstrate that the proposed model achieves further linearization performance improvements compared with other advanced models.