Machine Learning Based Linear and Nonlinear Noise Estimation
Machine Learning Based Linear and Nonlinear Noise Estimation
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DOI：
10.1364/jocn.10.000d42
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发表时间：
2018-10-01
影响因子：
5
通讯作者：
Savory, Seb J.
中科院分区：
文献类型：
--
作者：
Caballero, F. J. Vaquero;Ives, D. J.;Savory, Seb J.
运营商面临着最大限度地提高已部署链路的容量的压力。这可以通过在弱非线性状态下操作来获得，这需要对传输条件的精确理解。理想情况下，光转发器应该能够从接收到的信号中估计操作状态，并将该信息馈送到上层管理层以优化传输特性;然而，这种估计具有挑战性。本文通过估计接收信号的线性和非线性信噪比（SNR）来解决这个问题。这种估计是通过获得两个不同的效果：非线性相位噪声和二阶统计矩的功能。一个小的神经网络进行训练，估计信噪比从提取的功能。通过覆盖19，800组真实光纤传输的广泛模拟，我们验证了所提出的技术的准确性。同时采用这两种方法得到的线性和非线性信噪比的标准误差分别为0.04和0.20 dB的测量性能。
Operators are pressured to maximize the achieved capacity over deployed links. This can be obtained by operating in the weakly nonlinear regime, requiring a precise understanding of the transmission conditions. Ideally, optical transponders should be capable of estimating the regime of operation from the received signal and feeding that information to the upper management layers to optimize the transmission characteristics; however, this estimation is challenging. This paper addresses this problem by estimating the linear and nonlinear signal-to-noise ratio (SNR) from the received signal. This estimation is performed by obtaining features of two distinct effects: nonlinear phase noise and second-order statistical moments. A small neural network is trained to estimate the SNRs from the extracted features. Over extensive simulations covering 19,800 sets of realistic fiber transmissions, we verified the accuracy of the proposed techniques. Employing both approaches simultaneously gave measured performances of 0.04 and 0.20 dB of standard error for the linear and nonlinear SNRs, respectively.