## 2 Related Works  
In the past, RCSA methods, including both static and dynamic approaches, have been proposed to prevent the blocking of lightpath establishment while suppressing inter-core crosstalk.  
In [14], the authors proposed a static RCSA method with multi-input multi-output (MIMO)-based crosstalk suppression. The authors provided an Integer Linear Programming (ILP) model to show the effect of MIMO-based crosstalk suppression. The authors of [15] introduced a static RCSA method using the ILP formulation. It aims to minimize the maximum number of FSs on any core while ensuring a low level of inter-core crosstalk. The authors of [16] considered the asymmetric assignment of fiber cores in a counter-propagating manner to reduce inter-core crosstalk. They proposed a static RCSA method by formulating an ILP problem and developing a heuristic algorithm to optimize the utilization of network-spectrum resources. In [17], the authors addressed the scheduling of simultaneous lightpaths to reduce inter-core crosstalk. A new metric was introduced to estimate crosstalk by jointly considering spatial, spectral, and temporal dimensions, and an ILP model and a heuristic algorithm were developed to address the routing, spectrum, core, and time assignment problem. In [18], the authors introduced the design of multi-core fiber networks using shared-backup path protection, aiming to enhance both survivability and signal quality. They formulated a crosstalk-aware RCSA problem as an ILP model, minimizing spectrum usage and inter-core crosstalk. Furthermore, they proposed an auxiliary graph-based heuristic algorithm.  
The authors of [19] introduced a dynamic RCSA method based on the Connected Component Labeling (CCL) algorithm, which is commonly used in image processing. In this method, the spectrum of multi-core fibers is represented as a matrix, and the CCL algorithm efficiently identifies available spectrum regions for lightpath-setup requests with low computational complexity. In [20], the authors proposed a dynamic RCSA method that combines single- and multi-path routing with adaptive modulation and spectrum mapping. The method reduces the blocking probability and improves energy efficiency in dynamic scenarios while maintaining acceptable crosstalk and fragmentation levels. In [21], the authors proposed a dynamic RCSA method using a self-organizing feature mapping model to handle multi-dimensional constraints. By mapping core-level features to transmission quality, the method selects the optimal cores and identifies low-crosstalk spectrum blocks through a two-step classification. The authors of [22] introduced a 3D fragmentation metric, considering frequency, time, and space dimensions. Based on this metric, a dynamic RCSA scheme was developed to minimize future fragmentation and maximize core resource availability. The authors of [23] proposed an energy-efficient grooming and hybrid crosstalk solution algorithm to reduce both energy consumption and inter-core crosstalk. The algorithm selects candidate paths based on path length and load, applying grooming when energy efficiency conditions are met. For other requests, new paths are established using core prioritization and crosstalk-aware strategies. The authors of [24] introduced a dynamic RCSA method that aims to reduce the blocking probability of lightpath establishment by avoiding the use of cores where many FSs are already used while mitigating inter-core crosstalk by avoiding the use of the same FSs in adjacent cores. In [25], the authors proposed an on-demand spectrum and core allocation method based on two policies: core prioritization, which avoids adjacent core usage to reduce inter-core crosstalk, and core classification, which assigns uniform bandwidth connections to minimize fragmentation. They showed improved crosstalk performance and reduced blocking probability under various network conditions through simulation experiments.