Iterative Learning Control for Pre-distortion Design in Wideband Direct-Conversion Transmitters

A practical power amplifier (PA) has nonlinear characteristics that distort the output signal and hence increase the transmission error. Digital pre-distortion (DPD) has been widely accepted to compensate for the PA nonlinearity. However, in direct-conversion transmitters (DCTs), DPD performance is affected by in-phase and quadrature (IQ) imbalance. In this paper, we utilize the Iterative Learning Control (ILC) algorithm to design a DPD scheme to compensate for PA nonlinearity under IQ imbalance. We first prove that ILC is applicable in such a scenario. This proof is validated using simulations which show that ILC is able to estimate the PA ideal input. The estimated ideal input is then exploited in training a neural network (NN)-based DPD model. We provide the complexity estimation of our proposed scheme using the number of real multiplications. Finally, we demonstrate the performance advantage of our proposed scheme in comparison with other existing polynomial based approaches through simulations and measurements.