Digital fiber optic link monitoring has emerged as a pivotal technique for ensuring the high reliability, stability, and performance optimization of modern optical communication systems. With the continuous evolution of network architectures, driven by the exponential growth of data traffic, cloud computing, and emerging latency-sensitive applications, the demand for real-time, fine-grained monitoring of optical link conditions has become increasingly critical. Traditional performance monitoring schemes, which primarily focus on macroscopic indicators such as bit error rate and optical signal-to-noise ratio, offering only limited insight into the internal state of transmission links, are insufficient for meeting the requirements of intelligent and adaptive optical networks.

By leveraging advanced digital signal processing techniques at the receiver, digital fiber optic link monitoring enables the non-intrusive reconstruction and continuous observation of essential physical-layer parameters, including optical power distribution, chromatic dispersion, polarization-dependent loss, and nonlinear impairments. This capability provides network operators with real-time visibility into the link’s physical behavior, facilitating proactive fault diagnosis, predictive maintenance, and dynamic performance optimization. As optical networks evolve toward software-defined and self-optimizing architectures, digital link monitoring is expected to play a central role in supporting autonomous control, resource allocation, and closed-loop network management.

Substantial progress has been made in the development of digital fiber link monitoring techniques. Early studies primarily focused on correlation-matching-based power profile estimation (PPE), which utilized perturbation models combined with mirror-path construction to infer the spatial distribution of optical power along the fiber link. Although correlation method (CM)-PPE demonstrates the feasibility of in-band power profile estimation, its spatial resolution is limited, and the obtained results are often relative rather than absolute, constraining its practical applicability for fault localization and link performance analysis.

To address these shortcomings, the minimum mean square error (MMSE)-based power profile estimation method is subsequently proposed. This approach employs a least squares fitting framework to construct an unbiased estimator by minimizing the mean square error between the observed nonlinear signal distortions and those predicted by perturbation theory. Compared to earlier methods, MMSE-PPE significantly improves the accuracy of power profile reconstruction and can be readily extended to multi-parameter monitoring, including polarization-dependent loss and chromatic dispersion variations.

With increasing demand for robustness and adaptability in practical deployments, algorithmic enhancements have been introduced to further improve the performance of these estimation techniques. Regularization methods, spatial response function correction, and deconvolution strategies have been explored to mitigate estimation bias, suppress noise, and enhance the model’s resilience to complex and dynamic network conditions, such as amplifier gain fluctuations and polarization mode dispersion.

Digital backpropagation (DBP) reconstructs the signal propagation process by numerically solving the nonlinear Schr?dinger equation in the reverse propagation direction, thereby enabling the estimation of various transmission-induced impairments with high accuracy. The incorporation of deep neural networks into the DBP framework further enhances its adaptability, allowing it to dynamically optimize weight parameters and accommodate a wide range of link configurations and nonlinear distortion regimes. In addition to power profile estimation, DBP-based schemes have shown potential in amplifier gain spectrum analysis, and device behavior modeling, including wavelength-selective switch (WSS) effects, while simultaneously compensating for nonlinear impairments.

Furthermore, research efforts have extended digital monitoring frameworks toward multi-parameter estimation, targeting quantities such as chromatic dispersion, polarization mode dispersion, and differential group delay. These methods often employ template-based models in combination with advanced correlation matching techniques to facilitate comprehensive link state identification and classification under realistic network conditions.

Despite the promising achievements in digital fiber link monitoring, several technical challenges remain unresolved. Most notably, the computational complexity associated with high-accuracy inverse problem-solving remains a significant bottleneck, particularly for long-haul or high-capacity transmission systems where real-time estimation is essential. Moreover, many existing algorithms are inherently dependent on idealized link assumptions and may exhibit reduced robustness when confronted with complex, time-varying network environments.

Addressing these challenges will require future research efforts to prioritize algorithmic simplification and computational efficiency. The integration of deep learning models is expected to facilitate the development of adaptive, data-driven inversion frameworks capable of handling multi-parameter coupling effects and generalizing across diverse link conditions.

In summary, digital fiber optic link monitoring is transitioning from theoretical research to practical engineering applications and is expected to become an indispensable component of future intelligent optical networks. This technology will not only enhance the efficiency and reliability of optical transmission systems but will also enable advanced functionalities, including real-time fault detection, resource optimization, predictive maintenance, and autonomous self-healing. As the convergence of digital monitoring, machine learning, and sensing technologies continues to evolve, it is anticipated that digital fiber link monitoring will play a pivotal role in the development of next-generation self-optimizing and self-aware optical communication infrastructures.