Optimizing Optical Signal Quality with Deep Learning Dispersion Compensation at Various Distances

One of the limiting factors in long-distance communication with optical fiber is dispersion. Dispersion causes optical pulses to broaden as they travel through the fiber, leading to intersymbol interference that ultimately degrades the signal quality. To solve this problem, a dispersion compensator is usually applied after a certain distance to bring the broadened signal back to its original form. However, this conventional approach is ineffective in cost and implementation since more compensators should be added for a longer distance. In this paper, we propose a deep learning (DL)-based dispersion compensator as an alternative to the current compensator and supplement the current approach if the distance is very long. The dataset is created from signals obtained through the simulation done in optisystem. Different DL algorithms proposed in this paper can classify the received signals from various distances into binary values without the help of conventional dispersion compensation.