  We present a simple, efficient "direct learning" approach to train Volterra
series-based digital pre-distortion filters using neural networks. We show its
superior performance over conventional training methods using a 64-QAM 64-GBaud
simulated transmitter with varying transmitter nonlinearity and noisy
conditions.
