Time-Delay/Advance Neural Networks Based Digital Predistorters: Enabling High Efficiency and High Throughput Transmitter

The paper presents four deep feedforward neural network (FNN) models for digital predistortion of radiofrequency power amplifiers (PAs), namely real-valued time-delay/advance NN (RVTDANN), augmented real-valued time-delay/advance NN (ARVTDANN), residual real-valued time-delay/advance NN (R2TDANN) and augmented residual real-valued time-delay/advance NN (AR2TDANN). An experimental platform is implemented to evaluate their performance in terms of adjacent channel leakage ratio, normalized mean square error and error vector magnitude (EVM). The platform uses an envelope tracking PA excited by 160 MHz IEEE 802.11 WiFi signals. It is observed that the proposed models outperform the state-of-the-art FNN models by up to 3
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7 dB in EVM, using similar number of model coefficients. Moreover, the proposed models outperform the state-of-the-art recurrent neural network model by up to 0
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7 dB in EVM. When the PA operates close to its saturation point with a strong non-linear effect, the proposed RVTDANN and R2TDANN can almost achieve a transmit EVM of −42 dB. The observation indicates that the proposed models are key enablers for wireless transmitters enjoying high power efficiency and high throughput simultaneously.