  We present a simple nonlinear digital pre-distortion (DPD) of optical
transmitter components, which consists of concatenated blocks of a finite
impulse response (FIR) filter, a memoryless nonlinear function and another FIR
filter. The model is a Wiener-Hammerstein (WH) model and has essentially the
same structure as neural networks or multilayer perceptions. This awareness
enables one to achieve complexity-efficient DPD owing to the model-aware
structure and exploit the well-developed optimization scheme in the machine
learning field. The effectiveness of the method is assessed by electrical and
optical back-to-back (B2B) experiments, and the results show that the WH DPD
offers a 0.52-dB gain in signal-to-noise ratio (SNR) and 6.0-dB gain in optical
modulator output power at a fixed SNR over linear-only DPD.
