Optimizing FSO System Performance: Integrating Machine Learning for Security and Reliability

Free-space optical (FSO) communication is critical to next-generation data networks as it enables high data throughput without the frequency limitations of radio communication systems. However, the reliability and security of these systems can be severely compromised by environmental factors such as misalignment and unfavorable atmospheric conditions. This paper presents an innovative study using machine learning to analyze and optimize the trade-off between security and reliability in FSO communications. Utilizing a Meta Model approach, we systematically examine the impact of key operational parameters—such as signal strength, atmospheric turbulence, and alignment precision—on system performance. The results show that our approach can significantly improve the prediction of reliability and security, enabling more effective management of FSO transmission resources. This study opens new avenues for developing more robust and secure wireless optical communication systems by leveraging the potential of machine learning methods to address the challenges associated with FSO environments comprehensively.