Machine Learning for 100 Gb/s/λ Passive Optical Network

Responding to the growing bandwidth demand by emerging applications, such as fixed-mobile convergence for fifth generation (5G) and beyond 5G, 100 Gb/s/λ access network becomes the next research focus of passive optical network (PON) roadmap. Intensity modulation and direct detection (IMDD) technology is still considered as a promising candidate for 100 Gb/s/λ PON attributed to its low cost, low power consumption, and small footprint. In this paper, we achieve 100 Gb/s/λ IMDD PON by using 20G-class optical and electrical devices due to its commercial availability. To mitigate the system linear and nonlinear distortions, neural network (NN) based equalizer is used and the performance is compared with feedforward equalizer (FFE) and Volterra nonlinear equalizer (VNE). We introduce the rules to train and test the data while using NN-based equalizer to guarantee a fair comparison with FFE and VNE. Random data have to be used for training, but for test, both random data and pseudorandom bit sequence are applicable. We found that the NN-based equalizer has the same performance with FFE and VNE in the case of linear distortion, but outperforms them in a strong nonlinearity case. In the experiment, to improve the loss budget, we increase the launch power to 18 dBm, achieving a 30-dB loss budget for 33 GBd/s PAM8 signal at the system frequency response of 16.2 GHz, attributed to the strong nonlinear equalization capability of NN.