Wiener-Hammerstein model and its learning for nonlinear digital pre-distortion of optical transmitters.

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 perceptrons. 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.

Real-time 400 Gbps/carrier WDM transmission over 2,000†km of field-installed G.654.E fiber.

We combine erbium-doped fiber amplifier (EDFA) and backward distributed Raman amplifier (DRA) to achieve the real-time wavelength division multiplexing (WDM) transmission of 400 Gbps/carrier polarization division multiplexing (PDM) 16 quadrature amplitude modulation (QAM) signals over 2,000 km of terrestrial field-deployed cut-off shifted fiber (CSF) compliant with ITU-T G.654.E. This paper compares the transmission performance of 400 Gbps/carrier signals achieved in CSF and standard single-mode fiber (SMF). This transmission distance, 2,019 km, is, to the best of our knowledge, the longest in 400 Gbps/carrier WDM transmission field experiments using digital signal processing (DSP) application specific integrated circuit (ASIC) integrated real-time optical transponders with the technologies to compensate device imperfections; the backward DRA used is fully compliant with laser power safety requirements.