Fiber dispersion causes pulse broadening and signal distortion. Existing dispersion compensation approaches depend on standardized dispersion parameters at specific wavelengths (e.g., 1550 nm), which often mismatch actual fiber dispersion, leading to residual dispersion. We develop a Sagnac ring interferometry and electro-optic modulation system, combined with machine learning, to accurately characterize the C-band dispersion curve of a G.652D fiber, and inversely design a chirped fiber Bragg grating (CFBG) for tailored compensation. However, when attempting to quantify the residual dispersion numerically, conventional differentiation methods yield physically implausible results. Monte Carlo simulations confirm this fundamental unreliability, yielding a 95% confidence interval of 319,605 ps/(nm·km). To circumvent this limitation, we propose a joint evaluation method based on refractive index flatness and group delay uniformity. Within 1545–1555 nm, both indicators fluctuate by no more than 0.015% relative to their means, confirming that residual dispersion has been effectively suppressed. This approach provides a precise, personalized compensation mechanism applicable to optical fibers with individual dispersion characteristics, offering a controllable path for adaptive dispersion compensation in high-speed communication systems.
Yang et al. (Fri,) studied this question.