Joint OSNR monitoring and modulation format identification in digital coherent receivers using deep neural networks

Abstract

We experimentally demonstrate the use of deep neural networks (DNNs) in combination with signals' amplitude histograms (AHs) for simultaneous optical signal-to-noise ratio (OSNR) monitoring and modulation format identification (MFI) in digital coherent receivers. The proposed technique automatically extracts OSNR and modulation format dependent features of AHs, obtained after constant modulus algorithm (CMA) equalization, and exploits them for the joint estimation of these parameters. Experimental results for 112 Gbps polarization-multiplexed (PM) quadrature phase-shift keying (QPSK), 112 Gbps PM 16 quadrature amplitude modulation (16-QAM), and 240 Gbps PM 64-QAM signals demonstrate OSNR monitoring with mean estimation errors of 1.2 dB, 0.4 dB, and 1 dB, respectively. Similarly, the results for MFI show 100% identification accuracy for all three modulation formats. The proposed technique applies deep machine learning algorithms inside standard digital coherent receiver and does not require any additional hardware. Therefore, it is attractive for cost-effective multi-parameter estimation in next-generation elastic optical networks (EONs).

A Simple Joint Modulation Format Identification and OSNR Monitoring Scheme for IMDD OOFDM Transceivers Using K-Nearest Neighbor Algorithm

“…It is clear from the figure that OSNR estimates are quite accurate for both OBTB and 25 km SSMF configurations. Hence, the mean OSNR estimation error for the three signal types considered in this work is 0.69 dB, which is similar to the ones reported for the OSNR monitoring [18]. It is worth mentioning that the proposed algorithm can also work for coherent optical systems and single-carrier modulation.…”

“…Thus, as long as the corresponding OSNR under each RoP is known, OSNR value can be predicted according to different AH under different RoP. Consider that KNN has the function of regression prediction [25,26], which can be used in achieving OSNR monitoring [18]. Since identification accuracy is affected by the number of training samples and the k value of the KNN algorithm, two different sets of samples and k value, being 30, 1 (case 1) and 60, 3 (case 2) are introduced in the construction of the classifier.…”

“…In recent years, several machine learning-based modulation format identification (MFI) techniques have been proposed both in digital coherent and directly detected receivers [13][14][15][16][17][18][19][20][21][22] for optical communications systems because of their excellent learning ability from data, which can avoid the requirement of pre-information. Khan proposed a deep machine learning method to identify three modulation formats at an accuracy of 100% in a wide optical signal-to-noise ratio range [13].…”

“…However, these methods focus merely on identifying the modulation format of the received signal and do not provide any information about the quality of signal in terms of OSNR. Joint OSNR monitoring and modulation format identification in digital coherent receivers using deep neural network (DNN) was performed in [18]. The excellent experimental effect of DNN was considered, but the problem of high complexity was neglected.…”

Intelligent optical performance monitor using multi-task learning based artificial neural network

“…In [13], AHs of direct detected signals was selected as input features of artificial neural network (ANN) for MFI. In [10], the authors extended the work in [13]. AHs after constant module algorithm (CMA) were used to monitor OSNR and identify modulation format simultaneously with four ANNs.…”