Review of Machine Learning Techniques for Enhancing the Performance of OAM FSO Systems

In recent years, the advancement of Free Space Optical (FSO) communication systems has significant attention due to their potential for high-speed data transmission. This review examines the integration of ma-chine learning techniques in enhancing the performance and reliability of Orbital Angular Momentum (OAM) FSO systems. By machine learning (ML) algorithms, these systems can effectively mitigate challenges such as atmospheric turbulence, alignment issues, and signal attenuation. The paper highlights various ML methodologies employed in FSO systems, including supervised, unsupervised, and reinforcement learning approaches. It also discusses the comparative advantages of these techniques in optimizing signal quality, reducing error rates, and improving overall system efficiency. Through an in-depth analysis of current research and developments, this review provides insights into the future directions in the field of FSO communications facilitated by ML techniques.