The Role of Machine Learning and Deep Learning Approaches to Improve Optical Communication Systems  

作　　者：Weam M. Binjumah Weam M. Binjumah(Applied College of Taibah University, Madina, Saudi Arabia)

机构地区：[1]Applied College of Taibah University, Madina, Saudi Arabia

出　　处：《Journal of Intelligent Learning Systems and Applications》2024年第4期418-429,共12页智能学习系统与应用（英文）

摘　　要：In recent years, there has been a revolution in the way that we transmit information through optical communication systems, allowing for fast and high-capacity data transmission using optical communication systems. Due to the growing demand for higher-capacity and faster networks, traditional optical communication systems are reaching their limits due to the increasing demand for faster and higher-capacity networks. The advent of machine learning and deep learning approaches has led to the emergence of powerful tools that can dramatically enhance the performance of optical communication systems with significant efficiency improvements. In this paper, we provide an overview of the role that machine learning (ML) and deep learning can play in enhancing the performance of various aspects of optical communication systems, including modulation techniques, channel modelling, equalization, and system optimization methods. The paper discusses the advantages of these approaches, such as improved spectral efficiency, reduced latency, and improved robustness to impairments in the channel, such as spectrum degradation. Additionally, a discussion is made regarding the potential challenges and limitations associated with using machine learning and deep learning in optical communication systems as well as their potential benefits. The purpose of this paper is to provide insight and highlight the potential of these approaches to improve optical communication in the future.In recent years, there has been a revolution in the way that we transmit information through optical communication systems, allowing for fast and high-capacity data transmission using optical communication systems. Due to the growing demand for higher-capacity and faster networks, traditional optical communication systems are reaching their limits due to the increasing demand for faster and higher-capacity networks. The advent of machine learning and deep learning approaches has led to the emergence of powerful tools that can dramatically enhance the performance of optical communication systems with significant efficiency improvements. In this paper, we provide an overview of the role that machine learning (ML) and deep learning can play in enhancing the performance of various aspects of optical communication systems, including modulation techniques, channel modelling, equalization, and system optimization methods. The paper discusses the advantages of these approaches, such as improved spectral efficiency, reduced latency, and improved robustness to impairments in the channel, such as spectrum degradation. Additionally, a discussion is made regarding the potential challenges and limitations associated with using machine learning and deep learning in optical communication systems as well as their potential benefits. The purpose of this paper is to provide insight and highlight the potential of these approaches to improve optical communication in the future.

关 键 词：Machine Learning Deep Learning Optical Communication Design Science Research Literature Review 

分 类 号：H31[语言文字—英语]