Deep Reinforcement Learning (DRL) is rising as a promising tool for solving optimization problems in optical networks. Though studies employing DRL for solving static optimization problems in optical networks are appearing, assessing strengths and weaknesses of DRL with respect to state-of-theart solution methods is still an open research question. In this work, we focus on Routing and Wavelength Assignment (RWA), a well-studied problem for which fast and scalable algorithms leading to better optimality gaps are always sought for. We develop two different DRL-based methods to assess the impact of different design choices on DRL performance. In addition, we propose a Multi-Start approach that can improve the average DRL performance, and we engineer a shaped reward that allows efficient learning in networks with high link capacities. With Multi-Start, DRL gets competitive results with respect to a state-of-the-art Genetic Algorithm with significant savings in computational times. Moreover, we assess the generalization capabilities of DRL to traffic matrices unseen during training, in terms of total connection requests and traffic distribution, showing that DRL can generalize on small to moderate deviations with respect to the training traffic matrices. Finally, we assess DRL scalability with respect to topology size and link capacity.

Filterless Optical Networks (FONs) represent a novel cost-effective solution for metro optical networks, that allows to achieve equipment-cost savings by removing expensive optical-switching components from network nodes. In this study, we investigate how to further reduce equipment cost in FONs by minimizing amplifiers' cost. We propose a Genetic Algorithm (GA) for placing boosters, inline amplifiers and pre-amplifiers in FONs with the objective of minimizing amplifiers cost. We provide two versions of the GA and compare their performance against a baseline amplifier placement in terms of amplifiers cost and quality-of-transmission (QoT), i.e., lightpaths OSNR and received power. Moreover, we provide a comparison between filterless and wavelength-switched architectures. Simulative results achieved over realistic network topologies show significant amplifier cost savings, up to 60% compared to baseline approaches.

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.

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Emerging 5G services are revolutionizing the way operators manage and optimize their optical metro networks, and the metro network design process must be rethought accordingly. In particular, minimizing network cost is crucial to curb operators' investment. Taking advantage of relatively-short distances in metro networks, operators have the opportunity to optimize the placement of optical amplifiers (OAs) with the goal of minimizing amplifiers' cost (and hence decrease network cost) without significantly affecting the quality of transmitted optical signals. Minimizing OA cost translates not only in minimizing the cost of equipment (i.e., boosters, pre-amplifiers and inline amplifiers), but also in minimizing deployment and maintenance costs of active amplifier sites. In this paper, we propose a heuristic algorithm for OA placement and for the Routing and Spectrum Assignment (RSA) in metro networks, with the objective of minimizing the total cost of OAs while guaranteeing sufficient optical signal-to-noise ratio (OSNR) of established lightpaths. In our approach, we consider different cost for the deployed OAs, according to their location and type, i.e., inline amplifiers (ILAs), boosters and pre-amplifiers, and compare our optimized placement against benchmark strategies where OAs are predeployed at network nodes and at a fixed distance one from the other along optical fiber links. We also evaluate the impact of different routing strategies on the total cost and utilized spectrum. Simulative results, performed over realistic metro network topologies, show that our strategy provides up to 47% OAs cost savings while satisfying minimum OSNR constraints.