Digital predistortion with advance/delay neural network and comparison with Volterra derived models

This paper is focused on digital predistortion using neural networks (NNETs) for linearization of power amplifiers. We propose a new architecture of NNET. It is based on a feedforward tapped delay line neural network for complex signals with one hidden layer but it includes both delayed and advanced samples at its input. We name this architecture TADNN (tapped advance and delay line neural net). We show that the introduction of advance taps improves the digital predistortion (DPD) performance. We also compare the TADNN predistorter with predistorters derived from Volterra series such as memory polynomial or dynamic deviation reduction models. This comparison is based on three elements: performance in linearization, complexity, increase of the peak to average power ratio (PAPR) by the predistorter. Indeed, one drawback of Volterra based predistorters is that they can generate predistorted signals with very high PAPR that cannot be applied directly at the input of the power amplifier. Conversely, the PAPR of the predistorted signals at the output of the TADNN remains moderate. The presented approach is evaluated on a real LDMOS (laterally diffused metal oxide semiconductor) push-pull power amplifier with 16 MHz bandwidth OFDM (orthogonal frequency-division multiplex) signal at a carrier frequency of 200 MHz.