TY  - JOUR
AU  - Rahman, Md. Moklesur
AU  - Alam, Md. Nazmul
AU  - Faisal, Tarek Mah
AU  - Hossain, Md. Najmul
PY  - 2026
DA  - 2026/01/13
TI  - Dispersion-Compensating Method for High-Capacity Fiber-Optic Communication System Using Machine Learning Optimization
JO  - ICCK Transactions on Advanced Computing and Systems
T2  - ICCK Transactions on Advanced Computing and Systems
JF  - ICCK Transactions on Advanced Computing and Systems
VL  - 2
IS  - 1
SP  - 53
EP  - 60
DO  - 10.62762/TACS.2025.603512
UR  - https://www.icck.org/article/abs/TACS.2025.603512
KW  - surrogate optimization
KW  - simulation-driven machine learning
KW  - Random Forest regression
KW  - intelligent network management
KW  - configuration selection
KW  - advanced computing systems
AB  - Simulation-driven optimization of complex engineering systems increasingly demands intelligent frameworks capable of replacing exhaustive parameter sweeps with rapid, learned predictions. This paper presents a general surrogate optimization framework that couples high-fidelity simulation with a machine learning regression model to predict system performance and select optimal configurations without exhaustive re-simulation. Fiber-optic dispersion compensation is adopted as a representative benchmark: selecting the optimal placement strategy for dispersion-compensating fiber (DCF)-pre-, post-, or symmetrical-across varying system parameters is computationally expensive, making it an ideal testbed for surrogate-assisted design. A structured dataset is constructed from systematic OptiSystem simulation of DCF-compensated single-mode fiber (SMF) links across bit rates of 2.5-10~Gbps and fiber lengths of 80--160~km, with Q-factor and bit error rate (BER) serving as ground-truth labels. A Random Forest (RF) regression model is trained on this dataset to learn the nonlinear mapping from system parameters to performance outcomes. The framework achieves a prediction accuracy of 97.7% while reducing configuration search time by 91.2% relative to exhaustive simulation. Pre-compensation is consistently identified as the optimal DCF scheme, providing data-driven confirmation of this design preference. The proposed framework is domain-agnostic and generalizes to any simulation-intensive configuration task where labeled performance data can be systematically generated.
SN  - 3068-7969
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Rahman2026Dispersion,
  author = {Md. Moklesur Rahman and Md. Nazmul Alam and Tarek Mah Faisal and Md. Najmul Hossain},
  title = {Dispersion-Compensating Method for High-Capacity Fiber-Optic Communication System Using Machine Learning Optimization},
  journal = {ICCK Transactions on Advanced Computing and Systems},
  year = {2026},
  volume = {2},
  number = {1},
  pages = {53-60},
  doi = {10.62762/TACS.2025.603512},
  url = {https://www.icck.org/article/abs/TACS.2025.603512},
  abstract = {Simulation-driven optimization of complex engineering systems increasingly demands intelligent frameworks capable of replacing exhaustive parameter sweeps with rapid, learned predictions. This paper presents a general surrogate optimization framework that couples high-fidelity simulation with a machine learning regression model to predict system performance and select optimal configurations without exhaustive re-simulation. Fiber-optic dispersion compensation is adopted as a representative benchmark: selecting the optimal placement strategy for dispersion-compensating fiber (DCF)-pre-, post-, or symmetrical-across varying system parameters is computationally expensive, making it an ideal testbed for surrogate-assisted design. A structured dataset is constructed from systematic OptiSystem simulation of DCF-compensated single-mode fiber (SMF) links across bit rates of 2.5-10~Gbps and fiber lengths of 80--160~km, with Q-factor and bit error rate (BER) serving as ground-truth labels. A Random Forest (RF) regression model is trained on this dataset to learn the nonlinear mapping from system parameters to performance outcomes. The framework achieves a prediction accuracy of 97.7\% while reducing configuration search time by 91.2\% relative to exhaustive simulation. Pre-compensation is consistently identified as the optimal DCF scheme, providing data-driven confirmation of this design preference. The proposed framework is domain-agnostic and generalizes to any simulation-intensive configuration task where labeled performance data can be systematically generated.},
  keywords = {surrogate optimization, simulation-driven machine learning, Random Forest regression, intelligent network management, configuration selection, advanced computing systems},
  issn = {3068-7969},
  publisher = {Institute of Central Computation and Knowledge}
}