ML-Assisted Equalization for 50-Gb/s/λ O-Band CWDM Transmission Over 100-km SMF

We propose and demonstrate a bidirectional Vanilla recurrent neural network (Vanilla-RNN) based equalization scheme for O-band coarse wavelength division multiplexed (CWDM) transmission. Based on 4 × 50-Gb/s intensity modulation direct detection (IM/DD) system, we the significantly better bit error rate (BER) performance of Vanilla-RNN over conventional decision feedback equalizer (DFE) both Nyquist on-off keying (OOK) 4-ary pulse amplitude (PAM4) formats. It is shown that capable compensating linear nonlinear impairments induced by transceiver single-mode fiber (SMF). As result, up to 100-km 75-km SMF transmission can be achieved OOK PAM4 transmission, respectively. Furthermore, through comparison with other schemes, including equalizer, 3 rd -order Volterra Volterra+DFE, it demonstrated achieves best BER performance. In meantime, also exhibits lower implementation complexity when compared Volterra-based schemes. Our results show viable solution realizing simple effective equalization. This work serves as an exploration offers useful insights future implementations reach-extended CWDM IM/DD systems.