Abstract

Inter-symbol interference (ISI), mode coupling (MC) and other impairments present in high baud rate mode division multiplexing (MDM) systems significantly degrade the reliability and accuracy of optical signal transmission. Therefore, performing joint multi-parameter optical performance monitoring (OPM) is crucial to ensure the efficient operation of the system. We propose a scheme that combines asynchronous single channel sampling (ASCS) images with a multi-task residual neural network (MT-ResNet) to enable cost-effective joint OPM at intermediate nodes in a 100 GBaud MDM system. The scheme requires only one photodetector to receive the signal. Based on the distinct characteristics exhibited by ASCS images for different parameters in the MDM system, the MT-ResNet is improved to capture correlations among parameters and learn residual information between inputs and outputs. This enables high-precision and rapid simultaneous implementation of modulation format identification (MFI), MC estimation, optical signal-to-noise ratio (OSNR) estimation, and chromatic dispersion identification (CDI) in the intermediate nodes of a 100 GBaud MDM system. The effectiveness of the proposed scheme is verified using a 100 GBaud QPSK/8QAM/16QAM/32QAM/64QAM MDM simulation transmission system with five spatial modes (LP01/LP11a/LP11b/LP21a/LP21b). The results show that under the influence of different linewidth (LW) of laser and differential mode group delay (DMGD), a 100 % identification success rate can be achieved for five commonly used MFs and eight different cumulative CD values in high baud rate MDM systems, across a wide range of OSNR. Additionally, the mean absolute error (MAE) of OSNR estimation corresponding to the five MFs is 0.22 dB, 0.26 dB, 0.30 dB, 0.32 dB, and 0.36 dB, respectively. The MAE of the eight different MC intensities fluctuate between 0.015 and 0.030. These results fully demonstrate the superior performance of the proposed scheme. Furthermore, the scheme exhibits favorable tolerance to large LWs and fiber nonlinear effects. Moreover, the complexity of the scheme is accurately analyzed through floating-point operations (FLOPs) and parameters (Params), which are much less than existing typical schemes, with specific values of 14.8849 M and 1.3798 M.

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