## 2 Related Works  
The majority of optimization studies in SS-FONs have concerned the problem of lightpath provisioning for a set of traffic demands in static (offline) lightpath planning and for individual connection requests in dynamic (online) lightpath provisioning [8]. The lightpath provisioning problem in SS-FONs translates into the RSSA optimization problem, which was shown to be \(\mathcal{NP}\)-hard [8]. The RSSA-related optimization problems can be modeled as MIP problems [13, 15, 16, 17]. MIPs yield exact, globally optimal solutions; however, their scalability is usually low, which means that they cannot provide even feasible solutions for larger RSSA problem instances in a reasonable time. Therefore, both heuristics [13, 17, 18, 19] and meta-heuristics [13, 19] have been proposed to generate solutions to large-scale RSSA problems. For more related works concerning the RSSA problem and the optimization of SS-FONs, we refer to our literature survey in [8]. Regarding the XT-aware optimization of SS-FONs, two main approaches were considered, namely: (a) avoidance of XT by means of specific spatial and spectral resource allocation strategies [20, 21] and (b) estimation of XT levels during the RSSA process [12, 13, 17, 18, 22, 23]. The former approach does not guarantee that the XT levels of lightpaths are below the allowable thresholds. Therefore, these methods are not appropriate for highly loaded networks, in which the occupancy of spatial-spectral resources is dense. Contrarily, in the latter approach, the RSSA process is supported by the knowledge about the actual XT levels of both existing and potential (e.g., considered by an heuristic) lightpath allocations. In this scope, two basic methodologies for XT estimation were applied, namely: (a) worst-case XT [13, 17, 22, 23] and (b) precise XT [12, 14, 18, 24]. The worst-case XT estimation represents the maximum XT level that may occur in the MCF [13, 14], or in particular cores [14], regardless of the state of the network. Although this method is simple, as the XT estimation is static and not dependent on lightpath allocations, still, it may lead to an inefficient use of resources due to overestimation of the actual XT levels, as shown in [14]. In contrast, the precise XT estimation approach accounts for the actual allocation of spatial and spectral resources in network links. The main disadvantage of this method is its computational effort, as any change in the network state involves the need for XT check for each potentially affected lightpath. The estimation of transmission quality of lightpaths during the RSSA process, in order to verify whether they are acceptable, requires reliable analytical models. In many works concerning MCF-based SS-FONs (e.g., see [11, 12, 17, 22, 25, 26]), the QoT verification is reduced to the estimation of crosstalk using the analytical model presented in [9]. Since these studies do not consider other linear and nonlinear physical-layer impairments that affect the transmission of optical signals in fibers, the accuracy of QoT estimation is limited in these works. In [13, 14], an extended QoT model is used, which apart from using the model from [9] for XT verification, it accounts for the existence of other PLIs that may limit the maximum transmission reach (TR) of lightpaths in the network. However, in this approach, the QoT verification is still performed separately with regard to XT and other PLIs. Namely, first, it is checked whether the length of the lightpath is below the TR, and then crosstalk is estimated for this lightpath. In fact, as discussed in [10], the XT impairment is a source of OSNR degradation and, therefore, it should be integrated with other PLIs in an OSNR-based model for reliable QoT estimation. In our recent work [24], we have used such an integrated OSNR model in a dynamic lightpath provisioning scenario. However, to the best of our knowledge, the application of this model in the optimization of SS-FONs has not been considered in the literature yet.