# Going Deeper into OSNR Estimation with CNN
###### Abstract  
As optical performance monitoring (OPM) requires accurate and robust solutions to tackle the increasing dynamic and complicated optical network architectures, we experimentally demonstrate an end-to-end optical signal-to-noise (OSNR) estimation method based on the convolutional neural network (CNN), named OptInception. The design principles of the proposed scheme are specified. The idea behind the combination of the Inception module and finite impulse response (FIR) filter is elaborated as well. We experimentally evaluate the mean absolute error (MAE) and root-mean-squared error (RMSE) of the OSNR monitored in PDM-QPSK and PDM-16 QAM signals under various symbol rates. The results suggest that the MAE reaches as low as 0.125 dB and RMSE is 0.246 dB in general. OptInception is also proved to be insensitive to the symbol rate, modulation format, and chromatic dispersion. The investigation of kernels in CNN indicates that the proposed scheme helps convolutional layers learn much more than a lowpass filter or bandpass filter. Finally, a comparison in performance and complexity presents the advantages of OptInception. OSNR; optical performance monitor; convolutional neural network +
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rates. Moreover, the Refs. [11; 12; 13; 14] estimated carrier-to-noise ratios (CNR) or signal-to-noise ratios (SNR) to calculate OSNR based on statistical moments of equalized signals. In [15], the utilization of empirical cumulative distribution function (CDF) of the signal's amplitude overcame the degradation for signals employing multilevel constellations in moment-based OSNR estimation approaches mentioned above. In essence, this algorithm still estimates the SNR using [PERSON]'s method to fit theoretical CDF to the empirical one. However, the requirement of equalization on signals leads to these methods being designed for the digital coherent receiver. The cost of receiving and equalization makes these methods non-economic and even impossible in the intermediate network nodes. Besides, conventional techniques have limitations in simultaneous and independent monitoring of multiple transmission impairments, of which the effects are often physically inseparable [4]. Fortunately, machine learning (ML) becomes a promising alternative for realizing highly impairment-tolerant and adaptive monitoring with easy deployment. The emergence of deep learning made machine learning flourish in pattern recognition, speech recognition, and natural language processing. End-to-end deep learning has been used in waveform-to-symbol decoding in coherent fiber-optic communication [16]. The deep neural network (DNN) used in deep learning has proven to be effective in OSNR estimation [3]. By and large, the trend of deep learning methods in OPM can be listed as follows:  
1. Transparency to symbol rate, modulation format and impairments;
2. Joint estimation of multiple parameters;
3. Independence of signal receiving;
4. Robust performance along with low complexity;
5. End-to-end learning. The majority of monitoring methods utilizing deep learning can be concluded as a combination of a statistic diagram or a statistic of received signals and one kind of neural network. The diagram can be an eye diagram [17], asynchronous delay-tap sampling (ADTS) 2D histogram [18], or amplitude histogram (AH) [19; 20], while the statistic can be an error vector magnitude (EVM) [21] or Godard's error [22]. The neural network can be an artificial neural network (ANN), k-nearest neighbors (KNN), support vector machine (SVM), or convolutional neural network (CNN). These methods have one common flaw that they all need to do extra information extraction or feature extraction manually. The Refs. [23; 24; 25] showed a CNN method that needs asynchronously sampled raw data from analog-to-digital converters (ADC) as input.