Joint OSNR monitoring and modulation format identification on signal amplitude histograms using convolutional neural network

Abstract

Abstract A joint optical signal-to-noise ratio (OSNR) monitoring and modulation format identification (MFI) is proposed based on signal amplitude histograms (AHs) by using convolutional neural network (CNN). By the use of the proposed scheme, the OSNR monitoring accuracy can be achieved 97.6%, 100%, and 100% for 28GBaud QPSK, 8-QAM, and 16-QAM respectively within a wide OSNR range (10–26\xa0dB). Similarly, the MFI accuracy can all achieve 100% for the three modulation formats. In addition, the effect of the network structure on the CNN performance is also studied. In the proposed scheme, OSNRs and modulation formats can be simultaneously identified in high accuracy by using a single CNN without additional hardware assistance, which should be attractive for cost-effective multiple parameters estimation in future dynamic optical networks.