COMPARATIVE ANALYSIS OF ML-SCHEMES IN OWC SYSTEMS

With the advancement of wireless communication, optical wireless communication (OWC) has evolved into a communication method with a wide range of applications. However, different nonlinear effects in optical wireless communication will cause significant signal processing issues and degrade the system's performance. When paired with the visible light of machine learning methods, machine learning has a lot of potential for tackling nonlinear problems. Communication technology must be extremely valuable in terms of research. Traditional machine learning techniques like KGmeans, DBSCAN, and support vector machines (SVM) have been proven to perform well in pre-equalization, post-equalization, anti-system jitter, and phase correction in studies. The aforementioned methodologies are discussed and introduced, as well as their applications in the field of optical wireless communication signal processing, in the hopes of providing a reference for machine learning to handle numerous nonlinear problems in optical wireless communication. Because of its high nonlinear fitting capabilities, the deep neural network (DNN) can help the OWC system operate even better.