Performance Investigation of Modulation Format Identification in Super-Channel Optical Networks

Abstract

The problem of automatic modulation format identification (MFI) is one of the main challenges in adaptive optical systems. In this work, we investigate MFI in superchannel optical networks. The investigation is conducted by considering the classification of seven multiplexed channels of 20 Gbaud, each with six commonly used modulation formats, including polarization division multiplexing (PDM)-BPSK, PDM-QPSK, and PDM-MQAM with (M=8,16,32,64). The classification performance is assessed under different values of optical signalto-noise ratio (OSNR) and in the presence of channel interference, channel chromatic dispersion, phase noise, and 1 st polarization mode dispersion (PMD). Furthermore, the effect of fiber nonlinearity on the MFI accuracy is investigated. A well-established machine learning algorithm based on histogram features and a convolutional neural network has been used in this investigation. Results indicate that accurate identification accuracy can be achieved within the OSNR range of practical systems and that the MFI accuracy of side subcarriers outperforms that of middle subcarriers at a fixed value of OSNR. The results also show that the MFI accuracy of PDM-16QAM and PDM-64QAM are affected more by channel interference than the other modulation formats, especially when the ratio of the subcarrier bandwidth to subcarriers spacing is ≥ 1.4. Finally, laboratory experiments have been conducted for validation purposes. The experimental results were found in good agreement with those achieved by simulation.

Survey on Applications of Machine Learning in Low-Cost Non-Coherent Optical Systems: Potentials, Challenges, and Perspective

“…Many features for direct detection have a 2D structure: ADTP, ASCS, and eye diagrams, to name three examples. This fact has triggered strong interest in developing CNN algorithms to perform OPM and MFI/BRI [31,40,41,[84][85][86][87][88]91]. Overall, this wealth of literature could be viewed from the perspective of target type, for it derives the choice of learning settings (regression, classification, multi-task, .…”

“…The authors in ref. [91] tackle the MFI problem in new optical network settings, specifically super-channel settings. They propose a simple CNN trained to predict modulation format from Inphase-Quadrature Histograms (IQHs).…”

Achievable information rate optimization in C-band optical fiber communication system

“…This phenomenon is more obvious when higher-order modulation formats are applied. Generally, a higher modulation format means a higher symbol error rate (SER) [ 3 , 4 ]. However, using high-order modulation format can transmit more bits by each symbol.…”

Identification of advanced optical modulation formatand estimation of signal-to-noise-ratio based on parallel-twin convolutional neural network

“…In Ref. [10], modulation formats were successfully distinguished based on the two-dimensional in-phase quadrature histogram features and a convolutional neural network (CNN). However, methods reported in Ref.…”

“…However, methods reported in Ref. [9,10] can only identify the modulation formats of polarization division multiplexing (PDM) signals. In Ref.…”