The availability of coarse-resolution cost-effective Optical Spectrum Analyzers (OSA) allows its widespread deployment in operators’ networks. In this paper, we explore several machine learning approaches for soft-failure detection, identification and localization that take advantage of OSAs. In particular, we present three different solutions for the two most common filter-related soft-failures; filter shift and tight filtering which noticeably deform the expected shape of the optical spectrum. However, filter cascading is a key challenge as it affects the shape of the optical spectrum similarly to tight filtering; the approaches are specifically designed to avoid the misclassification of properly operating signals when normal filter cascading effects are present. The proposed solutions are: $i) multi-classifier$ approach, which uses features extracted directly from the optical spectrum, $ii) single-classifier$ approach, which uses pre-processed features to compensate for filter cascading, and $iii) residual-based$ approach, which uses a residual signal computed from subtracting the acquired single by OSAs from an expected signal synthetically generated. Extensive numerical results are ultimately presented to compare the performance of the proposed approaches in terms of accuracy and robustness.

The absence of electrical regenerators in transparent WDM networks significantly contributes to reduce the overall network cost. In transparent WDM networks, a proper resource allocation requires that the presence of physical impairments in routing and wavelength assignment (RWA) and lightpath provisioning be taken into account. In this article a centralized, a hybrid centralized-distributed and two distributed approaches that integrate information about most relevant physical impairments in RWA and lightpath provisioning are presented and assessed. Both centralized and hybrid approaches perform a centralized path computation at the management-plane level, utilizing physical impairment information, while the lightpath provisioning is done by the management plane or the control plane, respectively. The distributed approaches fall entirely within the scope of the ASON/GMPLS control plane. For these two approaches, we provide functional requirements, architectural functional blocks, and protocol extensions for implementing either an impairment-aware real-time RWA, or a lightpath provisioning based on impairment-aware signaling.

Future transport networks will have to cope with the continuous growth of IP traffic. Furthermore, transport networks need to evolve so as to drastically reduce both deployment costs and operating expenses. A reasonable strategy to achieve this goal consists of simplifying the network architecture by reducing the number of layers. Assuming a peer model IP over optical network, we propose an integrated routing strategy that takes into account constraints and dynamic occupancy of both the IP and optical layers. The collaboration of both layers in the routing process leads to optimization of network performance. The main emphasis is on the implementation requirements of this grooming functionality using GMPLS-TE mechanisms. Simulation results show the benefits obtained by applying this strategy.

An all-fiber device for the control of the state of polarization (SOP) is presented. The controller is high speed (its time response is less than 50 ms in the worst case), low loss (insertion losses are 2.5 dB independently of the input SOP), and low cost (is constructed with standard single-mode fiber). The polarization controller has been implemented using two Faraday rotators and works at 1550 nm. We have also designed an algorithm that confers an endless performance to the device. The controller has been included in a 2.5 Gb/s CPFSK optical coherent system and results are presented here.