Knowledge distillation technique enabled hardware efficient OSNR monitoring from directly detected PDM-QAM signals

Abstract

Deep neural network (DNN) enabled optical SNR (OSNR) monitoring is vital to facilitate the management of agile optical networks. However, both the scale size and the implementation complexity of DNNs prevent their practical application. Here, we experimentally demonstrate the knowledge distillation technique (KDT) for DNN-based OSNR monitoring from the directly detected polarization division multiplexed 16 quadrature amplitude modulation (PDM-16QAM) and PDM-64QAM signals by the use of amplitude histograms. First, both floating-point operations (FLOPs) and parameters (Params) are introduced into the fiber optic communications as the evaluation metrics for the computational complexity of DNNs. Then, the pre-trained teacher network and the to-be-used student network constitute a KDT network, while the task of the pre-trained teacher network transfers the knowledge to the student network. From the experimental results, we learn that the KDT benefits the student network and realizes a rms error (RMSE) of 0.98 dB over OSNR ranges of 14–24 dB and 23–34 dB for 10 Gbaud PDM-16QAM and PDM-64QAM signals, respectively. The Params of the student network are only 0.004M, and the FLOPs are 0.25M, which are less than that of existing OSNR monitoring schemes. Meanwhile, an RMSE improvement of 0.23 dB arising in the OSNR monitoring has been secured to the student network with the help of KDT. Therefore, we believe our proposed KDT is promising for optical performance monitoring with a lightweight DNN.