Fully-blind Neural Network Based Equalization for Severe Nonlinear Distortions in 112 Gbit/s Passive Optical Networks

Since PONs are primarily used for fiber-to-the-home (FTTH), the end-user transceivers must be cheap and power efficient while covering the increasing demand of data rates. For this reasons, they typically rely on intensity-modulation and direct-detection (IM/DD) of the optical signal. Current research is focusing on data rates beyond recent 50G-PON standardization efforts [1], i.e., towards PONs which are capable of delivering 100 Gbit/s [2]. Since cost-effective hardware hinders increasing the symbol rate, the focus shifts towards higherorder modulation formats such as 4-ary pulse amplitude modulation (PAM4). However, compared to conventional on-off-keying (OOK), which is used until 50G-PON, multi-level modulation formats are more prone to nonlinerities and, due to its reduced signal-to-noise ratio (SNR) tolerance, require optical amplification. The utilized low-cost semiconductor optical amplifiers (SOA) distort the signal at high received optical power (ROP) due to nonlinear gain saturation, which reduces the dynamic range [3].