RESUMO
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.
RESUMO
A field trial of 100-Gbit/s Ethernet over an optical transport network (OTN) is conducted using a real-time digital coherent signal processor. Error free operation with the Q-margin of 3.2 dB is confirmed at a 100 Gbit/s Ethernet analyzer by concatenating a low-density parity-check code with a OTN framer forward error correction, after 80-ch WDM transmission through 6 spans x 70 km of dispersion shifted fiber without inline-dispersion compensation. Also, the recovery time of 12 msec is observed in an optical route switching experiment, which is achieved through fast chromatic dispersion estimation functionality.
