Pre-compensating digital-to-analog converter impairments with an LSTM neural network

The performance of high-speed coherent communication heavily depends on digital-to-analog converters (DACs) when they operate within bandlimited and nonlinear regimes. We examine neural network (NN) based digital pre-distortion (DPD) methods to compensate the memory effects that are intertwined with the nonlinear response of the DAC. Using a long short-term memory (LSTM) NN architecture, we examine 8-level amplitude shift keying (ASK-8) at 64 Gbaud. We demonstrate experimental improvements of 1.6 dB and 2 dB in signal-to-noise ratio (SNR) compared to the Volterra and linear solutions, respectively.