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Elastic Optical Networking enables efcient use of spectral resources at the cost of a large parameter space which needs to be optimized to maximize transmission bandwidth. By formulating this optimization problem as a Markov Decision Process, we show that by using a state of the art reinforcement learning algorithm an agent can be trained, which is able to select near optimal parameters for different link conditions within seconds.Furthermore the trained agent is able to generalize to unseen conditions, removing the need to optimize and train for every possible link scenario.

Deep reinforcement learning (DRL) enables autonomic optical networking by allowing the network control and management systems to self-learn successful networking policies from operational experiences. This paper proposes a transfer learning approach for effective and scalable DRL in optical networks. We first present a modular DRL agent design to facilitate retrieving and transferring relevant knowledge between tasks requiring different dimensions of network state data. In particular, we partition network state data into common states, which contain generic information critical to multiple tasks (e.g., the spectrum utilization on fiber links), and task-specific states that are only used by a specific task (e.g., the utilization of virtual network functions). Separate neural network blocks are employed to process different state data. Based on the modular agent design, a multi-task learning (MTL) aided knowledge transferring scheme is proposed. The proposed scheme trains an MTL agent that can master multiple tasks simultaneously and thus enables to learn and transfer better-generalized knowledge across tasks. We perform case studies on the proposed transfer DRL approach considering two scenarios, namely, knowledge transferring between routing, modulation and spectrum assignment (RMSA) tasks for different networks and knowledge transferring from RMSA tasks to anycast service provisioning tasks. The DRL designs for RMSA and anycast service provisioning, including the state, action, and reward formulations and the training mechanisms, are also elaborated. Performance evaluations under both scenarios show that the proposed approach can effectively expedite the training processes of the target tasks and improve the ultimate service throughput.

The fiber-optic components described in earlier chapters are assembled into systems to provide communication services. This chapter takes a closer look at basic system concepts that were introduced in Chapter 3. To understand telecommunications systems and the emerging optical network, you need to learn both the specifics of how fiber-optic systems transmit signals and the tasks these systems perform. This chapter is the first of several that will teach you about optical networks and the services they provide.

Fiber-optic systems are no longer passive pipes that merely carry optical signals from point to point. Signals may be modulated in intensity, switched between fibers, shifted in wavelength, and modified in other ways. The potential of “optical networking” was over-promoted during the telecommunications bubble, but the need to operate on signals is growing with the volume of traffic and the size of the global telecommunications network. The devices that perform these operations on signals are called active components. You have already learned about several types of active components. Light sources, transmitters, receivers, optical amplifiers, and wavelength converters are all active components. In this chapter you’ll learn about optical modulators, optical switches, and other components that dynamically affect optical signals.

Machine learning (ML) has attracted researchers to discover numeral solutions in the field associated with optical networking problems. In this paper, ML discipline procedures have been discussed to proficiently execute Routing and Wavelength Assignment (RWA) for contribution in traffic calculation in the Wavelength Division Multiplexing (WDM) optical set-up. The growing demand for data transport through WDM networks has created problems related to the search for routes and the assignment of wavelengths in these networks. In optical networks, RWA is a well-known problem. To address this problem, researchers have proposed simple to complex heuristic machine learning algorithms. This paper describes how machine learning support can be used and shared in optical networks and the assessment of transmission quality (QoT), data traffic patterns, and crosstalk detection to help route and distribute resources. The RWA algorithm assessments rely on performance measures such as the blocking probability, network utilization, etc. The paper summarizes future research trends for the use of routing and distribution of resources in machine learning processes in optical networks with the results obtained.

Optical communication uses light signals for transmitting information from source to destination. Optical fiber communication has been more popular for long-distance data transfer in recent years due to various benefits such as low loss, low cost, simple amplification, minimal interference, and lightweight. A simple optical telecommunication system consists of a transmitter, a medium, and a receiver. Wavelength Division Multiplexing (WDM) is a significant improvement in optical communication. WDM is basically used for improving spectral efficiency and to handle more data from several clients. Normal WDM, CWDM, and DWDM are three types of WDM technology. DWDM uses the most denser optical channels in optical networking. Complete detail about all types of WDM is given. The optical spectrum used in the transmission of light signals is also discussed.

Optical filter based on liquid crystal on silicon (LCOS) is widely used in wavelength selective switch (WSS) to allocate the optical spectrum flexibly. Constrained by the achievable spectral resolution of single grating used in the WSS, the roll-off bandwidth of optical filter is challenging to improve for the purpose of precise and efficient spectrum allocation. In this article, we demonstrate a novel double gratings configuration to realize higher spectral resolution by repeatedly enlarging and accumulating the dispersion, under the condition of the maintenance of the final light spot size. After examining the filtering performance with respect to the diffraction number, we are able to achieve a spectral resolution of 1.63 GHz, indicating of almost tenfold enhancement in comparison with traditional single grating configuration. In particular, an LCOS-based optical filter with a spectral roll-off bandwidth of 1.26 GHz and addressable resolution of 187 MHz is demonstrated. Meanwhile, the insertion loss and the range of operation wavelength are investigated to determine the optimal number of diffractions for various applications. The proposed LCOS-based filter enables spectral manipulation of GHz roll-off bandwidth, fulfilling the performance requirements of future flexible optical networking.

Network virtualization (NV) is considered to be a promising solution to address the ossification of the current Internet infrastructure. With the recent advancements in optical virtualization, network virtualization can be further extended to virtualization-enabled optical substrate networks that supply connectivity-as-a-service . This integration of NV and optical virtualization is referred to as optical-based network virtualization in this work, which warrants a future-proof Internet that is not only technology-friendly (i.e, due to the service and hardware decoupling in NV) but also bandwidth-abundant (i.e., with optical networking). In optical-based network virtualization, it remains to resolve the classic virtual network embedding (VNE) problem (i.e., a NP-Complete problem) with the extra dimension of complexity from physical characteristics of the specific type of optical network (e.g., WDM or SLICE). In this work, we study this variant of the VNE problem, namely Optical Virtual Network Embedding (OVNE), in SLICE-based network virtualization. We avoid addressing the OVNE problem with simple add-on constraints to VNE but address it with two strategies: first, exploring the OVNE problem structure at different granular of network elements (e.g., path-channel and channel graph) to mitigate the complexity; second, designing a path growing process that solves the OVNE model with substantially reduced variables. The proposed approach is evaluated and compared to other representative path-based schemes with demonstrated improvements. The proposed approach can also obtain near-optimal solution to the OVNE problem with guaranteed closeness to the optimal solution.

The advanced communication networks require heterogeneous emerging technologies to be combined while enabling various future applications. The integration of 5G wireless and optical technology is considered an unavoidable approach to reach this goal. Based on 5G mobile communications and densification of cells, the upcoming idea of smart city becomes feasible and put on a lot of attention from the research community due to its effect on everyday life’s improvement and modernization. The concept of a smart city should support everything from electrical grids to traffic management and requires the transmission of a huge amount of data. Smart city planning with a reliable communication infrastructure that can provide stringent network requirements is unfeasible without the joint of optical and wireless technologies. This paper aims to provide an overview of recent developments of advanced optical networking to provide 5G transport networks and their applications in connecting a huge number of devices in future smart city infrastructures. The implementation of optical technologies in 5G core networking open numerous questions of how wireless and optical can coexist to provide sophisticated future applications, such as the smart city concept. Within this research, we will provide the answers to some of the key related questions.

In many optical communication systems, in order to make the optical networking convenient and meet the application requirements, the optical terminals need to work in the same band and at the extremely closely adjacent working wavelengths. In practice, because of the light reflection and scattering of the wireless channels with the suspended particles, parts of the emitted optical signals will be reflected to the focal plane array (FPA). On the other hand, because the bandwidth of practical optical filters cannot be made narrow enough, or the sensitivity of FPAs is usually very high, the isolation between the transmitting and receiving optical paths in the terminal cannot be made high enough even if the optical filtering is used. Both the received optical signal from the remote terminal and the transmitted optical signal reflected by the terminal or wireless channels will be detected by the FPA at the same time. The reflected optical signal will interfere with the detection of the received optical signals from the far end, which seriously reduces the detection performance and the accuracy of the light spot centroid calculation. This paper proposes an integral control and increase-decrease counting statistical method for FPAs to eliminate the influence of the reflected optical signal on the signal detection and the noise mean value in the mean value of the total detection signal. The detection sensitivity of the received signal from the remote terminal, the isolation of the transmitting and receiving paths and the accuracy of the light spot centroid calculation are improved by this method. The Cramer-Rao lower bound is calculated and analyzed. In addition, the integral control and increase-decrease counting statistical algorithm is verified by the two software co-simulation, and the simulation results are given to illustrate the feasibility and performance of this method.

The ongoing digital transformation is bringing a pervasive diffusion of ultra-broadband, fixed-mobile connectivity, the deployment of cloud-native Fifth Generation (5G) infrastructures, edge and fog computing and a wide adoption of artificial intelligence. This transformation will have far-reaching techno-economic impacts on our society and industry. Nevertheless, this transformation is still laying its foundation in electronics and the impending end of Moore’s law. Therefore, looking at the future, a rethinking of the ways of doing computations and communications has already started. An extended adoption of quantum technologies is one possible direction of innovation. As a matter of fact, a first quantum revolution, started decades ago, has already brought quantum technologies into our daily lives. Indeed, today, a second revolution seems to be underway, exploiting advancements in the ability to detect and manipulate single quantum objects (e.g., photons, electrons, atoms and molecules). Among the different technological approaches, topological photonics is a rapidly growing field of innovation. Drawing inspiration from the discovery of the quantum Hall effect and topological insulators in condensed matter, recent advances in topological photonics hold a promising opportunity for optical networking and quantum computing applications.

The intent-based networking (IBN) paradigm targets defining high-level abstractions so network operators can define what their desired outcomes are without specifying how they would be achieved. The latter can be achieved by leveraging network programmability, monitoring, and data analytics, as well as the key assurance component. In this tutorial, we introduce the IBN paradigm and its application to optical networking, highlighting the benefits that machine learning (ML) algorithms can provide to IBN. Because the deployment of ML applications requires a specific orchestrator to create ML functions that are connected as ML pipelines, we show an implementation of such an orchestrator. Some challenges and solutions are presented for the generation of accurate synthetic data, proactive self-configuration, and cooperative intent operation. Illustrative examples of intent-based operation and numerical results are presented, and the obtained performance is discussed. The research leading to these results has received funding from the MICINN IBON (PID2020-114135RB-I00) project and from the ICREA Institution.

Optical communication management and control are transforming to integrate new capabilities such as intent-based network management, closed-loop control automation, and multi-stakeholder orchestration. These capabilities are driven by the new connectivity requirements between data centers to enable future generations of services: Beyond 5G (B5G) and 6G applications offered at the edges of optical networks. The next generation of optical network management and control architectures will entail Software-Defined Networking (SDN) principles for the disaggregation of future optical systems. The current optical network controllers and managers are intrinsically proprietary and, consequently, restricted in openness, scalability, and flexibility. This Ph.D. thesis investigates and proposes breakthrough software architectures with: (i) their control functions for the optical systems and (ii) their management functions for optical connection services of Open Disaggregated Optical Networks. After explaining SDN architectures in the context and the constraints of optical switching and transmission networks, the thesis explains the challenges of current optical networks to transition towards the control of Partially Disaggregated Optical Networks as a first step and the control of Fully Disaggregated Optical Networks as the ultimate step. Novel software-defined optical network automation platforms with control functions based on micro-services are described pragmatically, considering open-source software frameworks and several open forums providing their languages and their data models. Their protocols are being developed for devices, network topology, and communication services. Next, the thesis described how control functions are designed as cloud-native network functions (CNF), enabling continuous integration and continuous development of cloud-native optical networking platforms. Automated optical channel path computation functions as services a re first addressed. These optical channel path computation services are described by explaining how the routing constraints defined by the evolutions of optical system capabilities can be integrated into the path computation engines (PCE). Several PCE algorithms for optical channel routing and spectrum allocation are presented, and their performances are compared in terms of reasonable or possibly optimal spectrum allocation.Subsequent to the concepts of automated optical channel path computation functions as services, the thesis proposes automated optical channel defragmentation functions as services to re-arrange the placements of optical channels for better and possibly optimal use of the spectrum grid to gain resources.From the evaluations of these different container-based optical control functions, several optical channel control automation scenarios are described to prove their concepts using a network bench in a lab and demonstrate the potential applications of optical CNFs.Finally, the thesis concludes with the synthesis of these research works and the future challenges to make the control and the management of optical networks more unified and streamlined to enable optical communications to be designed and an asset as connectivity services for future generation services., La gestion et le contrôle des communications optiques se transforment pour intégrer de nouvelles fonctionnalités telles que la gestion de réseau basée sur l'intention, l'automatisation du contrôle en boucle fermée et l'orchestration multipartite. Ces fonctionnalités sont motivées par les nouvelles exigences de connectivité entre les centres de données (datacenters) pour permettre le déploiement de futures générations de services telles que les applications au-delà de la 5G (Beyond 5G or B5G) et 6G offertes à la périphérie des réseaux optiques. La prochaine génération d'architectures de gestion et de contrôle des réseaux optiques comportera des principes de mise en réseau définie par logiciel (SDN) en relation avec la désagrégation des futurs systèmes optiques. Les contrôleurs et gestionnaires de réseaux optiques actuels sont intrinsèquement propriétaires et sont donc limités en termes d'ouverture, d'évolutivité et de flexibilité. Cette thèse de doctorat étudie et propose des architectures logicielles disruptives avec : (i) leurs fonctions de contrôle pour les systèmes optiques et (ii) leurs fonctions de gestion pour les services de connexion optique des réseaux optiques ouverts désagrégés. Après avoir presenté les architectures SDN dans le contexte et les contraintes des réseaux de commutation et de transmission optiques, la thèse explique les défis techniques des réseaux optiques actuels évoluant vers le contrôle des réseaux optiques partiellement désagrégés comme première étape de transition; et vers le contrôle des réseaux optiques entièrement désagrégés comme étape ultime. La nouvelles plates-formes logicielles d'automatisation des réseaux optiques avec leurs fonctions de contrôle basées sur des micro-services sont décrites de manière pragmatique en tenant compte des logiciels libres ainsi que de plusieurs forums de standardisation définissant leurs langages, leurs modèles de données et leurs protocoles pour les équipements, la topologie du réseau et les services de communication. Ensuite, la thèse décrit comment les fonctions de contrôle sont conçues comme des fonctions de réseau virtualisées permettant une intégration et un développement continus des plateformes de réseaux optiques natives en nuage (Cloud). Les fonctions de calcul de chemins de canal optique automatisées conçues comme des services sont d'abord abordées. Ces services de calcul de chemin de canal optique sont décrits en expliquant comment les contraintes de routage définies par l'évolution des fonctionnalités des systèmes optiques peuvent être intégrées dans les moteurs de calcul de chemin (PCE). Plusieurs algorithmes PCE pour le routage des canaux optiques et l'allocation sur la grille spectral sont présentés et leurs performances sont comparées en termes d'allocation bonne ou éventuellement optimale sur le spectre optique. A la suite des concepts de fonctions de calcul de chemin de canaux optiques automatisées en conçues comme des services , la thèse propose des fonctions de défragmentation automatisée des canaux optiques conçues comme des services pour réarranger les placements des canaux optiques afin d’obtenir une meilleure et éventuellement optimale utilisation de la grille de spectre optique pour gagner des ressources. A partir des évaluations de ces différentes fonctions de contrôle optique basées sur des conteneurs, plusieurs scénarios d'automatisation du contrôle des canaux optiques sont décrits pour prouver leurs concepts en utilisant un banc réseau dans un laboratoire, et pour démontrer les applications potentielles des VNF optiques. Enfin, la thèse conclut sur la synthèse de ces travaux de recherche et les défis futurs pour rendre le contrôle et la gestion des réseaux optiques plus unifiés et rationalisés afin de permettre aux communications optiques d'être conçues et d'être un atout conçues comme des services de connectivité pour les services de future génération.

Development of network architectures in the field of optical telecommunications technologies is an indicator of changes in telecommunication education engineering. Conducting didactic classes requires hardware infrastructure and research in terms of teaching needs. In the paper we present DWDM network laboratory solution for telecommunication education engineering on the basis of the ADVA Optical Networking equipment. We have to adjust and configure telecommunication equipment for a didactic purpose. We present the core optical equipment, network structure, its configuration and laboratory exercises.

In our country near about 1/4 of the patients lose their lives due to interrupted health monitoring system. In most of the hospitals, doctor visits patients once or twice in a day. A situation might arise where the patient's health becomes critical in between that interval when a doctor is not available with a patient and the patient might lose their life. Therefore, to overcome these issues of existing system we are proposing health monitoring by using Light Fidelity (Li-Fi) technology, where patient's health is monitored and it will be updated to the doctor throughout the day. Li-Fi technology is a new emerging technology which uses wireless optical networking phenomenon for data transmission with data rate of 224Gbps. The use of Li-Fi technology for health-care monitoring system is that it can measure various physiological parameters of the human body and is secure, reliable and better than conventional Wireless Fidelity (Wi-Fi) technology. Results of the prototype model show better performance in terms of accessibility and portability of the physiological sensor. Proposed system is user friendly, cost effective and will have impact on the upcoming days in the hospitals.