Dear Colleagues,

Artificial intelligence (AI) has disrupted a comprehensive range of engineering and technologies segments. machine learning-based optical systems are also gaining more research attention, particularly in optoelectronics devices, communications, networking, nonlinear transmission systems, optical performance monitoring, and cross-layer network optimizations for photonics networks.

Optical machine learning (OML) can be employed to explore the challenges in the domain of optical fiber communications, namely: optical devices, optical waveguides, photonic crystal fiber, multicore fiber, radio over fiber, power over fiber, WDM, DWDM, and OFDM networks, such as nonlinear mitigation, characterization, performance optimization, testing, planning, fault prevention, network maintenance, quality of transmission (QoT), equipment realization performance prediction, network resources allocation and management. OML can also facilitate optimization in future photonics networks used for big data analytics. It can integrally acquire and reveal hidden patterns and correlations in big data, which can be extremely valuable for unraveling complex optical network optimization issues. OML-driven, next-generation optical networks can offer set-ups for monitoring themselves, analyzing and resolving their problems, and can provide intelligent and efficient services with minimal failure. The greatest challenge of optical communication is nonlinear distortions, which can be predicted and optimized using different, optical, machine learning-based algorithms. OML provides nonlinear mitigation, phase noise mitigation, signal recovery, pattern quantization, channel monitoring, channel estimation, performance enhancements for FSO (free-space optical communication), and guided systems. In the case of free-space optical communication, the greatest hurdle is atmospheric turbulence, which predicated and optimized using the deep learning algorithm. Machine learning techniques can conduct impairments monitoring in optical links without adaptive optics and other signal processing algorithms. Convolution neural networks (CNN) recognize orbital angular momentum (OAM) modes in turbulent FSO links, and support vector machines (SVM); ?-nearest neighbor (KNN)-based methods are also helpful for predicting noise in the channel. Optical machine learning (OML) techniques are implemented to compute PCF optical properties, nanophotonics structures, laser beam alignment, adaptive linear optic devices, ultrafast photonics, and medical application-based photonics devices.

This Special Issue will summarize the state-of-the-art methods so that researchers can validate ML practices for use as a distinctive and effective set of signal processing tools in optic communication systems. It aims to resolve critical issues that cannot be easily explored using conventional approaches. Furthermore, 5G networks will require a more dynamic and optimized network through increasing the implementation of AI and big data in future networks. Thus, such compressive material will become necessary and beneficial for optical communications and networking researchers.

Potential points incorporate yet are not restricted to the accompanying:

• Intelligence optical systems;
• Characterization of optical networks using AI/ML;
• Transmission estimation in an optical networks using ML;
• AI and on-board AI in optical networks;
• OML-based failure management in optical networks;
• Future intelligent elastic optical networks;
• OML for routing and resource allocation in optical networks;
• Mitigating nonlinearity issues in optical networks using AI;
• Traffic prediction using OML;
• Optimized photonics-based system;
• OML for free-space optical communication;
• Characterization of optoelectronic materials using ML;
• Nanophotonic devices using machine learning;
• Advancing optical communication systems using OML;
• Optical sensor characterization using OML;
• Multi dimension photonic crystal fiber using AI;
• Machine learning-based optical systems for medical applications;
• OML-based photonics devices.

Dr. Santosh Kumar
Dr. Ali Kashif Bashir
Dr. Ajay Kumar Vyas
Guest Editors

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• optical machine learning
• networking
• intelligence network
• AI
• ML
• optical communication
• elastic optical networks
• photonics devices
• nonlinearity