Mitigation of SOA-Induced Nonlinearities With Recurrent Neural Networks in 75 Gbit/s/λ PAM-4 IM/DD WDM-PON Transmission Systems
Ahmed Galib Reza, Marcos Troncoso Costas, Colm Browning, Sean O’Dúill, Liam P. Barry
Abstract
We experimentally demonstrate 4×75 Gbit/s optically amplified 4-level pulse amplitude modulation (PAM-4) transmission based on an external Mach-Zehnder modulator (MZM) and electro-absorption modulator (EAM) for monolithically integrable intensity modulation and direct detection wavelength division multiplexed passive optical networks (WDM-PONs). The effects of semiconductor optical amplifier (SOA)-induced nonlinear distortions during single- and multi-wavelength amplification in the optical distribution networks are investigated for various channel spacings through experiments and simulations. A machine learning-based nonlinear equalizer termed as a recurrent neural network (RNN) is proposed to compensate for the nonlinear impairments. Finally, by employing a T-spaced RNN in conjunction with a traditional feed-forward equalizer (FFE), we achieve link budgets in excess of 31 dB and 28 dB on every WDM channel at the hard-decision forward error correction (HD-FEC) limit of 3.8×10-3 for MZM and EAM-based WDM-PONs, respectively, after 4×75 Gbit/s PAM-4 transmissions at 1550 nm with a 100 GHz channel spacing over 25 km feeder and 1 km distribution single-mode fiber links.