Abstract
This letter proposes a residual-fitting modeling method for digital predistortion (DPD) of broadband power amplifiers (PAs), and then constructs a residual-fitting model. To avoid directly modeling strong nonlinearity and memory effect, the model is split into a conversion, fitting, and recovery module. In this way, the nonlinearity and memory effect of the output signal of PAs are reduced after the conversion module, and then the fitting module models the converted signal, finally the behavioral characteristics of PAs are recovered by the recovery module. In the experimental test, a 100 MHz orthogonal frequency division multiplexing (OFDM) signal is used as input signal of a Doherty PA. The experimental results show that compared with the existing augmented real-valued time-delay neural network (ARVTDNN), the proposed residual-fitting memory polynomial-ARVTDNN (MP-ARVTDNN) model with much fewer coefficients lowers normalized mean square error (NMSE) and adjacent channel power ratio (ACPR).
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