LAUSR

This brief proposes a digital signal recovery (DSR) method to compensate the nonlinear distortion introduced by power amplifiers (PAs) under dynamic nonlinear operating states. Unlike conventional PA linearization methods that extract the nonlinearity based on the baseband I/Q PA input and output signal samples, the proposed method attempts to derive the memory polynomial (MP) model parameters based on PA operating states using a deep neural network (DNN). This method allows the receiver to achieve DSR by tracking the operating states of the PA effectively with a few telemetry data. Validation results from simulations and experiments based on a GaN PA operating at 3.5 GHz reveal that the proposed method can maintain satisfactory DSR performance in terms of adjacent channel power ratio (ACPR) and error vector magnitude (EVM) while the transmitter PA is operating with fluctuating average input/output power, supply voltage, and bias voltage. The training data size and time are further reduced by using a transfer learning (TL) approach.