Joint and Accurate OSNR Estimation and Modulation Format Identification Scheme Using the Feature-Based ANN

Abstract

A joint and accurate optical signal-to-noise ratio (OSNR) estimation and modulation formats identification (MFI) scheme based on the artificial neural network (ANN) is proposed and demonstrated via both simulation and the experiment system. The proposed scheme employs ANN to estimate OSNR and modulation formats from the OSNR and modulation formats dependent features, kurtosis, and amplitude variance. Simulation results show that the proposed scheme can achieve high OSNR estimation and MFI accuracy over wide OSNR ranges for the commonly used modulation formats such as QPSK, 8 quadratic-amplitude modulation (QAM), 16QAM, and 64QAM. Meanwhile, experimental results also indicate that the mean OSNR estimation errors are 0.15 dB, 0.41 dB, and 0.49 dB for QPSK, 8QAM, and 16QAM over wide ranges OSNR of 10–17 dB, 14–20 dB, and 17–25 dB, respectively. Additionally, 100% MFI accuracy for the commonly used modulation formats in our scheme is also confirmed experimentally. Compared with the convolutional neural network and the deep neural network, the proposed scheme shows comparable estimation and identification performance, and needs less computational resource. Therefore, our scheme can be considered an attractive technique for joint OSNR estimation and MFI in future reconfigurable optical networks.

Keywords

• Artificial neural network
• feature
• kurtosis
• amplitude variance
• optical signal-to-noise ratio
• modulation format identification.