论文标题
在纤维传输实验中，神经网络的非线网络量很少量化
Few-bit Quantization of Neural Networks for Nonlinearity Mitigation in a Fiber Transmission Experiment
论文作者
论文摘要
在16 QAM 9x50km双极化光纤传递实验中，对神经网络进行了量化，以缓解非线性和组件变形。训练后添加剂在6位时量化会导致可忽略不计的Q因子罚款。在5位时，模型大小减少了85％，罚款为0.8 dB。
A neural network is quantized for the mitigation of nonlinear and components distortions in a 16-QAM 9x50km dual-polarization fiber transmission experiment. Post-training additive power-of-two quantization at 6 bits incurs a negligible Q-factor penalty. At 5 bits, the model size is reduced by 85%, with 0.8 dB penalty.
