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

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/lambda access network becomes the next priority for the passive optical network roadmap. We experimentally demonstrate the transmission of 100-Gbit/s/lambda 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.