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Artificial intelligence (AI) and machine learning (ML) continue to demonstrate substantial capabilities in solving a wide range of optical-network-related tasks such as fault management, resource allocation, and lightpath quality of transmission (QoT) estimation. However, the focus of the research community has been centered on ML models' predictive capabilities, neglecting aspects related to models' understanding, i.e., to interpret how the model reasons and makes its predictions. This lack of transparency hinders the understanding of a model's behavior and prevents operators from judging, and hence trusting, the model's decisions. To mitigate the lack of transparency and trust in ML, explainable AI (XAI) frameworks can be leveraged to explain how a model correlates input features to its outputs. In this paper, we focus on the application of XAI to lightpath QoT estimation. In particular, we exploit Shapley additive explanations (SHAP) as the XAI framework. Before presenting our analysis, we provide a brief overview of XAI and SHAP, then discuss the benefits of the application of XAI in networking and survey studies that apply XAI to networking tasks. Then, we model the lightpath QoT estimation problem as a supervised binary classification task to predict whether the value of the bit error rate associated with a lightpath is below or above a reference acceptability threshold and train an ML extreme gradient boosting model as the classifier. Finally, we demonstrate how to apply SHAP to extract insights about the model and to inspect misclassifications. [ABSTRACT FROM AUTHOR]

Increased global traffic puts tough requirements not just on fiber communications links but on the entire network. This manifests itself in multiple ways, including how to optimize wavelength routing around the network, how to maximize the benefits arising from fine-control DSP with increasingly accurate real-time monitoring, and how to best deploy multiband or multiple fiber connectivity. This article will summarize research into all these areas to present a full picture of how future optical networks will play their role in supporting the continuing traffic demands of broadband, 5G, and associated applications. [ABSTRACT FROM AUTHOR]

This Special Section (SI) features the latest research contributions regarding recent advances in the design and management of reliable communication networks. Communication networks are constantly increasing their complexity and scale to satisfy the requirements of network services. The current trend of convergence of networking and computing infrastructures (as in today’s cloud systems and softwarized networks) calls for novel advanced strategies and solutions to support reliable services, as the development of new data-driven solutions for reliable network automation and self-diagnostic tools to ensure resilient network management. [ABSTRACT FROM AUTHOR]

In presence of multiple failures affecting their network infrastructure, operators are faced with the Progressive Network Recovery (PNR) problem, i.e., deciding the best sequence of repairs during recovery. With incoming deployments of 5G networks, PNR must evolve to incorporate new recovery opportunities offered by network slicing. In this study, we introduce the new problem of Progressive Slice Recovery (PSR), which is addressed with eight different strategies, i.e., allowing or not to change slice embedding during the recovery, and/or by enforcing different versions of slice connectivity (i.e., network vs. content connectivity). We propose a comprehensive PSR scheme, which can be applied to all recovery strategies and achieves fast recovery of slices. We first prove the PSR’s NP-hardness and design an integer linear programming (ILP) model, which can obtain the best recovery sequence and is extensible for all the recovery strategies. Then, to address scalability issues of the ILP model, we devise an efficient two-phases progressive slice recovery (2-phase PSR) meta-heuristic algorithm, small optimality gap, consisting of two main steps: i) determination of recovery sequence, achieved through a linear-programming relaxation that works in polynomial time; and ii) slice-embedding recovery, for which we design an auxiliary-graph-based column generation to re-embed failed slice nodes/links to working substrate elements within a given number of actions. Numerical results compare the different strategies and validate that amount of recovered slices can be improved up to 50% if operators decide to reconfigure only few slice nodes and guarantee content connectivity. [ABSTRACT FROM AUTHOR]

Filterless Optical Networks (FONs) (i.e., optical networks where switching nodes are solely based on passive splitters and combiners) enjoy features that are highly appreciated by network operators, such as their low cost and their energy efficiency, posing them as an alternative solution to filtered Wavelength-Switched Optical Networks (WSON) based on active switching nodes. Due to FONs’ specific design criteria (the network topology must be divided into link-disjoint filterless fiber trees to avoid laser loops), traditional network problems, such as survivable virtual network mapping, shall be revisited and tackled adopting novel solutions with respect to state-of-the-art filtered WSONs. In this paper, we investigate the problem of survivable virtual network mapping (SVNM) in FONs with the aim of evaluating the cost of survivability when adopting FON technology. We first model the problem as an Integer Linear Program to establish fiber trees and provide survivable mapping of virtual networks, while minimizing cost of additional network equipment and spectrum with respect to WSON. We then propose multiple heuristic and meta-heuristic approaches to tackle large problem instances. In our numerical evaluations, we consider three scenarios: FON, WSON, and FON with pre-established fiber trees. Results show that in FON, where SVNM is jointly optimized with fiber tree establishment, the investment in additional network equipment can be largely minimized, and even avoided in some cases. In contrast, in FON with pre-established trees, amount of additional network equipment needed to guarantee survivability is significant (up to 60% with respect to WSON). [ABSTRACT FROM AUTHOR]

The recent acceleration in fiber-to-the-home deployment worldwide along with the emerging 5G communications are pressuring network operators to enhance their networks to serve these new deployments. Hence, operators are seeking new high-capacity optical-network architectures, while averting excessive capital and operational expenditures. Filterless optical networks (FONs), by replacing costly wavelength selective switches in switching nodes with passive optical power splitters/combiners, currently represent a prominent candidate for cost-effective optical-network deployment. In this tutorial, we provide an overview of the architecture and the design issues of FONs when deployed in core and in metro networks. We also perform a techno-economic study to quantify the economic benefits of FONs, comparing their cost to that of state-of-the-art filtered optical networks, and we discuss how several networking problems such as resource allocation, network slicing, and protection are tackled in the context of FONs. Finally, we present our vision of how research on FONs will evolve in the coming years. [ABSTRACT FROM AUTHOR]

Optical network failure management (ONFM) is a promising application of machine learning (ML) to optical networking. Typical ML-based ONFM approaches exploit historical monitored data, retrieved in a specific domain (e.g., a link or a network), to train supervised ML models and learn failure characteristics (a signature) that will be helpful upon future failure occurrence in that domain. Unfortunately, in operational networks, data availability often constitutes a practical limitation to the deployment of ML-based ONFM solutions, due to scarce availability of labeled data comprehensively modeling all possible failure types. One could purposely inject failures to collect training data, but this is time consuming and not desirable by operators. A possible solution is transfer learning (TL), i.e., training ML models on a source domain (SD), e.g., a laboratory testbed, and then deploying trained models on a target domain (TD), e.g., an operator network, possibly fine-tuning the learned models by re-training with few TD data. Moreover, in those cases when TL re-training is not successful (e.g., due to the intrinsic difference in SD and TD), another solution is domain adaptation, which consists of combining unlabeled SD and TD data before model training. We investigate domain adaptation and TL for failure detection and failure-cause identification across different lightpaths leveraging real optical SNR data. We find that for the considered scenarios, up to 20% points of accuracy increase can be obtained with domain adaptation for failure detection, while for failure-cause identification, only combining domain adaptation with model re-training provides significant benefit, reaching 4%–5% points of accuracy increase in the considered cases. [ABSTRACT FROM AUTHOR]

This Special Issue contains a collection of outstanding papers covering several recent advances in optical networks for 5G communications and beyond. Papers are organized into four categories: network resource planning; optical access networks; optical fronthaul solutions; and autonomous and data-driven network management. In this introduction, a brief overview of the field is given, followed by a summary of the seventeen papers of this Special Issue, and a discussion of future directions in the field. [ABSTRACT FROM AUTHOR]

The outbreak of coronavirus 2019 (Covid-19) has imposed a worldwide lockdown, changing the way people live and work, and pressuring the healthcare systems in many countries. Contact tracing based on smartphone apps (app-based contact tracing) has emerged as a possible solution to trace contagions and enforce more sustainable and selective quarantines. However, these apps require a very high adoption rate to reach the critical mass for effective contact tracing. As an alternative, network-based contact tracing, which exploits geo-localization in next generation networks (e.g., 5G) can be used by mobile operators (MOs) to passively trace users' mobility and contacts, provided that a targeted localization accuracy of down to one meter can be achieved. To effectively trace contagions, the identities of positive individuals, which are known by governmental authorities (GAs), are also required. Hence, in network-based contact tracing, MOs and GAs must exchange users' privacy-sensitive data, as geo-locations and infection status, to compute the likelihood that an individual has been infected. To address the privacy issues raised by network contact tracing, after presenting an overview of app-based vs. network-based contact tracing systems, we propose a protocol to make network-based systems compliant with stringent privacy requirements. From extensive simulations, we observe that the cost to guarantee privacy (evaluated in terms of data overhead introduced by the protocol) is acceptable. Finally, we elaborate on the advantages of the proposed system (e.g., more efficient monitoring of virus spread), as well as on the open issues that should be solved before its adoption (e.g., extensive research on potential privacy leakage is required). [ABSTRACT FROM AUTHOR]

Malicious jamming launched by smart jammers can attack legitimate transmissions, which has been regarded as one of the critical security challenges in wireless communications. With this focus, this paper considers the use of an intelligent reflecting surface (IRS) to enhance anti-jamming communication performance and mitigate jamming interference by adjusting the surface reflecting elements at the IRS. Aiming to enhance the communication performance against a smart jammer, an optimization problem for jointly optimizing power allocation at the base station (BS) and reflecting beamforming at the IRS is formulated while considering quality of service (QoS) requirements of legitimate users. As the jamming model and jamming behavior are dynamic and unknown, a fuzzy win or learn fast-policy hill-climbing (WoLF–CPHC) learning approach is proposed to jointly optimize the anti-jamming power allocation and reflecting beamforming strategy, where WoLF–CPHC is capable of quickly achieving the optimal policy without the knowledge of the jamming model, and fuzzy state aggregation can represent the uncertain environment states as aggregate states. Simulation results demonstrate that the proposed anti-jamming learning-based approach can efficiently improve both the IRS-assisted system rate and transmission protection level compared with existing solutions. [ABSTRACT FROM AUTHOR]