As emerging services have a higher demand for internet performance, high-capacity, multi-channel, and flexible fiber optic communication systems have become the trend of optical communications with the advantages of dynamic, high-capacity, and transparent transmission. Complex link impairments in large-capacity and multi-channel optical communication systems put forward higher requirements for optical performance monitoring (OPM) technology. The number of monitoring parameters and links of OPM needs to be increased continuously with a higher monitoring accuracy and a larger dynamic range. In the previous papers, existing monitoring mechanisms for optical fiber communications focus on OPM performance and are still dominated by single-channel monitoring schemes. The so-called multi-channel monitoring schemes are operated sequentially by selecting specific channels through tunable optical filters, which may introduce measurement delays for multi-channel systems such as wavelength division multiplexing (WDM) systems. Besides, in next-generation dynamically reconfigurable optical networks, OPM is also conducted on intermediate nodes except for the receiver. Obviously, there are few studies on this flexible OPM. In order to meet these demands for future OPM schemes, it is necessary to develop OPM that can be used for multi-channel monitoring with portability, low complexity, and high accuracy. Therefore, a simplified multi-channel parallel OPM scheme is proposed based on deep learning to overcome the shortcomings in multi-channel monitoring.

In this paper, a multi-channel parallel OPM scheme based on signal spectrum and multi-task deep neural network (MT-DNN) is proposed to deal with the shortcomings of the multi-channel OPM. This scheme processes the collected multi-channel spectrum from the fiber link by downsampling, filtering, signal waveform separation, and power normalization. Then, the number of signal sample points is counted based on each power value interval to generate amplitude histograms (Ahs). The Keras library in the TensorFlow deep learning framework (version 2.0) is used to build an MT-DNN model. Since Ahs reflect the statistical distribution of signal amplitude, the bin number vector of Ahs is used as the input of MT-DNN for training, which can realize the multi-channel modulation format identification (MFI) and optical signal-to-noise ratio (OSNR) monitoring of a WDM system. In order to further investigate the performance of this OPM scheme and cope with the complex transmission environment, a transfer learning-assisted multi-task deep neural network (TL-MT-DNN) is proposed for parallel monitoring of multi-channel MFI and OSNR. This paper shares the parameters of the MT-DNN model in the source domain (D_S) except for the output layer to the TL-MT-DNN model in the target domain (D_T) to replace random initialization of the network parameters. The parameters of the output layer of the TL-MT-DNN model are randomly initialized. The parameters of the TL-MT-DNN model are tuned for better monitoring performance by using Fine-Tuning, a parameter-tuning method commonly used in transfer learning.

The proposed MT-DNN model for multi-channel parallel MFI and OSNR monitoring is demonstrated in this paper. In the established three-channel WDM coherent optical communication system, an accurate monitoring with MFI accuracy of 100% and mean absolute error (MAE) of 0.16 dB for OSNR monitoring is achieved for three-channel signals with ten modulation formats combined by PDM-4QAM/16QAM/64QAM (Fig. 10 and Fig. 11). In order to deal with a more complex transmission environment, the paper transfers the parameters of MT-DNN to TL-MT-DNN to achieve parallel monitoring of multi-channel MFI and OSNR according to the principle described in Fig. 5. This scheme has better portability and saves a large number of samples and training epochs (Fig. 12). The MFI accuracy can reach 100%, and the MAE of three-channel OSNR monitoring is 0.24 dB, 0.20 dB, and 0.19 dB, respectively (Fig. 13). The results show that the simplified multi-channel parallel OPM scheme based on deep learning proposed in this paper can monitor the multi-channel optical system without processing each channel individually and requiring additional filtering equipment. The scheme can be extended to any node of the fiber optic link or receiver side to achieve multi-channel monitoring, which is suitable for future high-capacity and elastic optical transmission systems.

This paper proposes a multi-channel OPM technique based on signal spectrum and MT-DNN at the intermediate node of the WDM system for multi-parameter parallel monitoring of high-capacity multi-channel optical networks. The method can monitor multi-channel OPM without processing each channel individually. The performance of this scheme is demonstrated, and the scheme can accurately monitor multi-channel signals. The influence of hyperparameters of MT-DNN (weighting factor of each task, optimizer, and training set size) on its monitoring performance is studied. In order to verify the portability of this OPM scheme for complex transmission environments, a TL-MT-DNN model is proposed and demonstrated with a low training cost and low implementation complexity. The results show that the proposed intelligent OPM scheme requiring only one spectrometer and a single MT-DNN can achieve accurate multi-channel monitoring, which can be extended to any node of the fiber optic link or receiver side to achieve accurate monitoring. Due to these advantages, this method provides a certain research reference for future flexible and high-capacity optical network performance monitoring.