Low-Complexity Recurrent Neural Network Based Equalizer With Embedded Parallelization for 100-Gbit/s/λ PON

作者：Xiaoan Huang, Dongxu Zhang, Xiaofeng Hu, Chenhui Ye, Kaibin Zhang · 发表于：Journal of Lightwave Technology · 年份：2021 · DOI：10.1109/jlt.2021.3128579 · 被引用次数：33 · 研究领域：Optical Network Technologies、Advanced Photonic Communication Systems、Advanced Fiber Laser Technologies

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.