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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 signal acquired 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.

Spatially integrated switching architectures have been recently investigated in an attempt to provide switching capability for networks based on spatial division multiplexing (SDM) fibers, as well as to reduce the implementation cost. These architectures rely on the following switching paradigms, furnishing different degrees of spectral and spatial switching granularity: independent switching (Ind-Sw), which offers full spatial-spectral flexibility; joint-switching (J-Sw), which treats all spatial modes as a single entity; and fractional-joint switching (FrJ-Sw), whereby subgroups of spatial modes are switched together as independent units. The last two paradigms are categorized as spatial group switching (SG-Sw) solutions since the spatial resources (modes, cores, or single-mode fibers) are switched in groups. In this paper, we compare the performance (in terms of spectral utilization, data occupancy, and network switching infrastructure cost) of the SDM switching paradigms listed above for varying spatial and spectral switching granularities in a network planning scenario. The spatial granularity is related to the grouping of the spatial resources, whereas the spectral granularity depends on the channel baud rate and the spectral resolution supported by wavelength selective switches (WSS). We consider two WSS technologies for handling of the SDM switching paradigms: 1) the current WSS realization, 2) WSS technology with a factor-two resolution improvement. Bundles of single-mode fibers are assumed across all links as a near-term SDM solution. Results show that the performance of all switching paradigms converge as the size of the traffic demands increases, but finer spatial and spectral granularity can lead to significant performance improvement for small traffic demands. Additionally, we demonstrate that spectral switching granularity must be adaptable with respect to the size of the traffic in order to have a globally optimum spectrum utilization in an SDM network. Finally, we calculate the number of required WSSs and their port count for each of the switching architectures under evaluation, and estimate the switching-related cost of an SDM network, assuming the current WSS realization as well as the improved resolution WSS technology.