Low-cost low-margin implementation plays an essential role in upgrading optical metro networks required for future 5G ecosystem. In this regard, low-resolution analog-to-digital converters can be used in coherent optical transponders to reduce cost and power consumption. However, the resulting transmission systems become more sensitive to physical layer fluctuations like the events caused by fiber stressing. Such fluctuations might have a strong impact on the quality of transmission (QoT) of the signals. To guarantee robust operation, soft decision forward error correction (FEC) techniques are required to guarantee zero post-FEC bit error rate (BER) transmission, which could increase the power consumption of the receiver and thus operational expenses. In this paper, we aim at minimizing power consumption while keeping zero post-FEC errors by means of a predictive autonomic transmission agent (ATA) based on machine learning. We present a sophisticated ATA model that, taking advantage of real-time monitoring of state of polarization traces and the corresponding pre-FEC BER, predicts the right FEC configuration for short-term operation, thus requiring minimum power consumption. In addition, we propose a complementary long-term prediction of excessive pre-FEC BER to enable remote reconfiguration at the transmitter side through the network controller. A set of experimental measurements is used to train and validate the proposed ATA system. Exhaustive numerical analysis allows concluding that ATA based on artificial neural network predictors achieves the maximum QoT robustness with 80% power consumption reductions compared to static FEC configuration.

中文翻译：

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低成本低利润城域光网络的预测自主传输

低成本，低利润的实施在升级未来5G生态系统所需的光学城域网方面起着至关重要的作用。在这方面，低分辨率的模数转换器可用于相干光应答器中，以降低成本和功耗。但是，最终的传输系统对物理层波动（如由光纤应力引起的事件）更加敏感。此类波动可能会对信号的传输质量（QoT）产生重大影响。为了保证鲁棒的操作，需要使用软判决前向纠错（FEC）技术来保证FEC后的误码率（BER）传输为零，这可能会增加接收机的功耗，从而增加运营成本。在本文中，我们的目标是通过基于机器学习的预测自主传输代理（ATA）来最大限度地减少功耗，同时保持零后FEC错误。我们提出了一种先进的ATA模型，该模型利用对极化迹线状态和相应的pre-FEC BER的实时监控，为短期操作预测了正确的FEC配置，因此需要的功耗最小。此外，我们提出了对过量FEC前BER的补充长期预测，以通过网络控制器在发送器端实现远程重新配置。使用一组实验测量值来训练和验证所提出的ATA系统。