  To comprehensively assess optical fiber communication system conditions, it
is essential to implement joint estimation of the following four critical
impairments: nonlinear signal-to-noise ratio (SNRNL), optical signal-to-noise
ratio (OSNR), chromatic dispersion (CD) and differential group delay (DGD).
However, current studies only achieve identifying a limited number of
impairments within a narrow range, due to limitations in network capabilities
and lack of unified representation of impairments. To address these challenges,
we adopt time-frequency signal processing based on fractional Fourier transform
(FrFT) to achieve the unified representation of impairments, while employing a
Transformer based neural networks (NN) to break through network performance
limitations. To verify the effectiveness of the proposed estimation method, the
numerical simulation is carried on a 5-channel
polarization-division-multiplexed quadrature phase shift keying (PDM-QPSK) long
haul optical transmission system with the symbol rate of 50 GBaud per channel,
the mean absolute error (MAE) for SNRNL, OSNR, CD, and DGD estimation is 0.091
dB, 0.058 dB, 117 ps/nm, and 0.38 ps, and the monitoring window ranges from
0~20 dB, 10~30 dB, 0~51000 ps/nm, and 0~100 ps, respectively. Our proposed
method achieves accurate estimation of linear and nonlinear impairments over a
broad range, representing a significant advancement in the field of optical
performance monitoring (OPM).
