Intelligent Optical Communication System: Harnessing Reinforcement Learning Techniques for Improved Performance and Optimization
DOI:
https://doi.org/10.46610/ARCEI.2025.v02i01.003Keywords:
Communication system performance, Intelligent optical communication, Optical communication systems, Optical network optimization, Reinforcement learningAbstract
The increasing demand for high-speed and reliable data transmission has necessitated the development of Intelligent Optical Communication Systems. This study proposes optimizing optical communication systems using Reinforcement Learning (RL) techniques to enhance optical network performance. The Intelligent Optical Communication System leverages the RL algorithm to adaptively adjust system parameters to address network challenges resulting from bandwidth allocation, power management, adaptive modulation, routing inefficiency, inequalization, nonlinear impairment models, and Bit Error Rate (BER), to predictively maintain and enhance network performance and error minimization. We adopted a comprehensive simulation framework that includes quantitative assessments of various parameters. Results show that bandwidth allocation varied from 100 to 150 Mbps, while power allocation ranged between 10 to 30 dBm. Adaptive modulation strategies effectively utilized formats like QPSK, 16-QAM, and 64-QAM, resulting in dynamic modulation selection over time. The study recorded average routing delays up to 50ms and showcased the received signal's amplitude using equalization techniques. Nonlinear impairment modeling revealed a quadratic relationship between signal power and nonlinear impairment levels, indicating that higher signal powers could significantly degrade signal quality. The BER was approximately 2%, reflecting the system’s effectiveness in maintaining data integrity. Predictive maintenance analysis demonstrated varying failure probabilities and maintenance impacts, emphasizing the need for ongoing monitoring to reduce service interruptions. Conclusively, the overall results indicate that integrating RL into optical communication systems can significantly enhance operational efficiency and adaptability. Ultimately, these align with policy goals that focus on improving reliability and service quality in telecommunications. This work contributes to advancing optical communication technology, paving the way for more efficient and reliable data transmission systems in an increasingly data-driven world.
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