## 1 Introduction
In [12], the authors proposed an approach to optimize the weighted sum rate for users transmitting orthogonally, modeling the problem as a non-convex optimization challenge and solving it with Channel State Information (CSI) from both primary and secondary systems using Lagrange multipliers. Similarly, Ref. [13] addressed an optimization problem focused on energy efficiency involving resource and power allocation for Orthogonal Frequency-Division Multiple Access (OFDMA)-based hybrid CRNs, deriving closed-form solutions using the Lagrange dual decomposition method. Additionally, the study in [14] introduced a modified Ant Colony Optimization algorithm inspired by the foraging behavior of ants to solve the resource allocation problem in CRNs. Furthermore, the work in [15] presented a dynamic Medium Access Control (MAC) framework and optimal resource allocation strategy for multi-channel ad hoc CRNs, optimized using the Particle Swarm Optimization (PSO) algorithm. Despite these advancements, resource allocation in CRNs remains a complex and challenging task, often classified as NP-hard and typically yielding only near-optimal solutions [16]. In real-time operations, the limitations of globally constrained optimization become apparent due to high computational time and complexity. To address these challenges, CR users are expected to leverage learning capabilities to identify optimal strategies for CRNs. Recent progress in Artificial Intelligence (AI), especially in Machine Learning (ML) and RL, offers promising solutions for achieving efficient resource management strategies [17; 18; 19]. The study in [20] proposed an online Q-learning scheme for power allocation, where cognitive users employ predictive features to identify the power level that maximizes performance under given constraint. Another study in [21] introduced an asynchronous Advantage Actor-Critic (A3C)-based power control scheme for SUs, in which SUs learn power control policies concurrently across multiple CPU threads, reducing the interdependency of neural network gradient updates. Additionally, RL techniques have gained popularity in multi-agent environments and distributed systems. For instance, Ref. [22] presented a multi-agent model-free RL scheme for resource allocation, which mitigates interference and eliminates the need for a network model through decentralized cooperative learning. While RL has demonstrated its potential in enabling both PUs and SUs in CRNs to intelligently manage resources, its performance tends to degrade significantly as the scale of the wireless communication network increases [23]. This degradation is primarily due to RL algorithms typically representing the network state using Euclidean data structures, such as CSI matrices and user request matrices, which fail to fully capture the underlying topology of wireless networks. To address this limitation, recent research has focused on incorporating topological information to enable more effective learning. For instance, the study in [24] explored the application of spatial convolution techniques for scheduling in the context of the sum-rate maximization model, leveraging user location data. They proposed a novel graph embedding-based approach for link scheduling in Device-To-Device (D2D) networks, utilizing a K-Nearest Neighbor (KNN) graph representation method to reduce computational complexity. Due to its cognitive capabilities, a CR can interact with its environment in real time, enabling it to identify and adjust optimal communication parameters as the radio environment evolves. In essence, CR systems continuously analyze environmental data to determine the best possible transmission parameters that meet specific performance requirements. The accuracy of decisions made by CR systems is highly influenced by how the environmental input is represented. Furthermore, the selection of transmission parameters plays a critical role in determining overall spectral efficiency.