Fiber-optic communication and networks play an important role in communications technology. According to the last few decades, in this area, research development is growing rapidly. Machine learning (ML) algorithms for optical communications (OC) are certainly a hot topic in the current generation. To overcome the current limitation and different issues of fiber-optic communication and network, a machine learning (ML) algorithm is essential for us. Machine learning techniques are proof that it has superiority in solving complex problems. Machine learning algorithms generally focused on the education field, business organization, and health sectors. Currently, many researchers work in the optical communications area by using machine learning algorithms techniques. A machine learning algorithm is an emerging technology because it helps in the optical communication field for a better quality of service (QoS). According, to the first time, this work reviews machine learning for optical communication literature from a machine learning viewpoint. Only fiber-optic communication and network experts work on machine learning for optical networks, and they are not ML algorithms experts. This paper uses machine learning algorithms for calculating the quality of transmission (QoT) of light paths in optical 1 Springer Nature 2021 L A T E X template Article Title networks link. For better quality of transmission (QoT) estimation tools, it can show the performance analysis of machine learning-based algorithms by using such as bit error rate (BER), optical signal to noise ratio (OSNR), quality factor (Q-factor), blocking probability, and signal to noise ratio (SNR) data. This paper presents a novel concept of quality of transmission (QoT) based on the machine learning algorithm.
The optical network plays a vital role in the current technology for providing high-speed data communication, which the networks operate at Tb/s. In this case, different modulation techniques can be used for different line rates to achieve high-speed data transmission. The light paths and various data rates such as 10, 40, and 100 Gb/s are the important parameters for mixed line rate networks. The wavelengths and line rates are powerful tools for mixed line rates networks. It can exist on different optical fibers. In this paper, advanced modulation techniques achieve a relative performance with the required Q-factor. This paper analysis for different matrix computations to achieve a superior Q-factor. It can affect the data rates and quality of transmission. This paper also proposed an algorithm that can improve the Q-factor. Q-factor is analyzed and proposes routing and wavelength assignment (RWA) techniques based on the Q-factor obtained at different line rates. This paper is a brief overview of a quality-aware path-finding algorithm for mixed line rates WDM/DWDM networks are present and discussed.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.