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 super-channel 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 signal-to-noise ratio (OSNR) and in the presence of channel interference, channel chromatic dispersion, phase noise, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$1^{st}$</tex-math></inline-formula> 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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\geq$</tex-math></inline-formula> 1.4. Finally, laboratory experiments have been conducted for validation purposes. The experimental results were found in good agreement with those achieved by simulation.
Highlights
T He ever increasing demand of high speed applications such as internet protocol (IP) video delivery, cloud computing, and internet-of-everything put a great challenge on the existing optical networks [1]
1) We investigate the modulation format identification (MFI) in super-channel network under the effect of different channel impairments, including channel additive noise, chromatic dispersion (CD), phase noise (PN), polarization mode dispersion (PMD), fiber nonlinearity, and channel interference resulting from the simultaneous transmission of different signals over the channel
It is observed that the MFI accuracy for polarization division multiplexing (PDM)-BPSK, PDM-QPSK, PDM-8QAM, and PDM-32QAM is almost not affected by the increase of DGD as it remains greater than 98% at DGD = 45 and 35 ps for SOP = 5◦ and 25◦, respectively
Summary
T He ever increasing demand of high speed applications such as internet protocol (IP) video delivery, cloud computing, and internet-of-everything put a great challenge on the existing optical networks [1]. In MFI, the authors in [19], [20] proposed an identification technique based on artificial neural network (ANN) and amplitude histogram (AH) to identify different optical modulation formats. Though this method does not require timing recovery and additional hardware/circuits, it is not appropriate for classifying the high order modulation types (i.e. M-QAM, M > 16). Our investigation is equipped with a well-established ML algorithm; a CNN classifier trained with the two-dimensional (2D) in-phase quadrature histogram (IQH) features We selected such a technique for our investigation because the 2D-IQH and/or CNN have been effectively used in literature for MFI in other types of optical channels; e.g. single mode fiber, few mode fiber, and free space optical channels; see [8] and the references therein.
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