Abstract

To meet the demand of emerging applications, such as fixed-mobile convergence for the fifth generation of mobile networks and beyond, a 100-Gbit/s/λ access network becomes the next priority for the passive optical network roadmap. We experimentally demonstrate the transmission of 100-Gbit/s/λ intensity modulation and direct detection passive optical network based on four-level pulsed amplitude modulation in the O-band by using 25G-class optics. To mitigate the severe distortions caused by inter-symbol interference and fiber nonlinearity, a low-complexity recurrent neural network based equalizer with parallel outputs is proposed. Experimental results show that the proposed recurrent neural network equalizer can consistently outperform fully-connected neural network with the same input/output size and number of training parameters. The neural network equalizer's sensitivity against quantization is also evaluated. To further understand the complexity and actual hardware resource consumption of the parallel-output equalizers, we implement an 8bits-integer-quantized neural network model using FPGA, with the benefits and challenges validated and discussed.

Low-complexity Volterra-inspired neural network equalizer in 100-G band-limited IMDD PON system

Luyao Huang, Wenqing Jiang, Yongxing Xu, Weisheng Hu, and Lilin Yi
Opt. Lett. 47(21) 5692-5695 (2022)

Computational complexity comparison of feedforward/radial basis function/recurrent neural network-based equalizer for a 50-Gb/s PAM4 direct-detection optical link

Zhaopeng Xu, Chuanbowen Sun, Tonghui Ji, Jonathan H. Manton, and William Shieh
Opt. Express 27(25) 36953-36964 (2019)

Cascade recurrent neural network-assisted nonlinear equalization for a 100 Gb/s PAM4 short-reach direct detection system

Zhaopeng Xu, Chuanbowen Sun, Tonghui Ji, Jonathan H. Manton, and William Shieh
Opt. Lett. 45(15) 4216-4219 (2020)

High-throughput hardware deployment of pruned neural network based nonlinear equalization for 100-Gbps short-reach optical interconnect

Mingyuan Li, Wenjia Zhang, Qiao Chen, and Zuyuan He
Opt. Lett. 46(19) 4980-4983 (2021)

Recurrent neural networks achieving MLSE performance for optical channel equalization

Sai-Chandra-Kumari Kalla, Christian Gagné, Ming Zeng, and Leslie A. Rusch
Opt. Express 29(9) 13033-13047 (2021)