Implementing Neural Network-Based Equalizers in a Coherent Optical Transmission System Using Field-Programmable Gate Arrays
نویسندگان
چکیده
In this work, we demonstrate the offline FPGA realization of both recurrent and feedforward neural network (NN)-based equalizers for nonlinearity compensation in coherent optical transmission systems. First, present a pipeline showing conversion models from Python libraries to chip synthesis implementation. Then, review main alternatives hardware implementation nonlinear activation functions. The results are divided into three parts: performance comparison, an analysis how functions implemented, report on complexity hardware. Q-factor is presented cases bidirectional long-short-term memory coupled with convolutional NN (biLSTM + CNN) equalizer, CNN standard 1-StpS digital back-propagation (DBP) simulation experiment propagation single channel dual-polarization (SC-DP) 16QAM at 34 GBd along 17 × 70 km LEAF. biLSTM+CNN equalizer provides similar result DBP 1.7 dB gain compared chromatic dispersion baseline experimental dataset. After that, assess impact utilization when approximating using Taylor series, piecewise linear, look-up table (LUT) approximations. We also show mitigate approximation errors extra training provide some insights possible gradient problems LUT approximation. Finally, evaluate achieve 200G 400G throughput, fixed-point NN-based approximated developed implemented FPGA.
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ژورنال
عنوان ژورنال: Journal of Lightwave Technology
سال: 2023
ISSN: ['0733-8724', '1558-2213']
DOI: https://doi.org/10.1109/jlt.2023.3272011