## 1 Introduction  
With the deployment of sixth-generation mobile communication systems, users benefit from improved QoS and a wide range of high-quality services delivered at extremely high data rates [1]. However, despite these advancements, significant challenges remain (notably the explosive growth in data traffic and the scarcity of available spectrum resources [2]). The increasing demand for wireless applications and the inefficient utilization of the radio spectrum highlight the urgent need for innovative solutions, one of which is the implementation of cognitive radio (CR) technology. Recently, CR has gained considerable attention from both the industry and academia as a promising technology for dynamic spectrum management to increase the reliability and throughput of wireless communication systems. CRNs are intelligent wireless systems in which SUs, equipped with cognitive capabilities, dynamically adapt their transmission parameters based on the surrounding radio environment. This promotes a more dynamic and resource-efficient application of the available spectrum without interfering with licensed PUs [3]. In addition to spectrum sensing and access, CR introduces intelligent mechanisms for spectrum management and interference mitigation by analyzing and adapting to various communication scenarios [4; 5]. CR is an emerging paradigm for radio spectrum allocation that enables SUs to exploit underutilized transmission opportunities in the licensed spectrum, provided that their transmissions do not degrade those of PUs [6]. CRNs typically operate under three primary access models: (i) the underlay model, (ii) the overlay model, and (iii) a hybrid of the two. In the underlay model, concurrent transmission by SUs and PUs is permitted, given that the interference caused to PUs remains below a predetermined benchmark, commonly known as Interference Temperature, that defines acceptable interference levels [7]. In the overlay model, SUs exploit unused spectral bands not currently accessed by PUs, often using advanced signal processing or cooperation techniques. The hybrid model combines features of both. Among these, the underlay model is often preferred for its simplicity and efficient spectrum utilization, along with lower implementation complexity compared to the hybrid approach [8]. A significant number of recent studies have shown that spectrum utilization in underlay CRNs can be greatly enhanced through efficient resource allocation strategies. In such systems, the coexistence of Primary Base Stations (PBSs) and Secondary Base Stations (SBSs) introduces a major challenge known as Co-Channel Interference (CCI), which is a primary bottleneck in resource allocation. When PBSs and SUs utilize the same subchannel concurrently, severe CCI can occur due to three main interference sources: (i) interference from PUs to SUs, (ii) interference between SU-PU communication links and other devices, and (iii) interference among SUs themselves [9]. While the first type of interference is typically managed by setting a suitable threshold to ensure QoS for PUs, the other types are addressed through orthogonal transmission schemes, power control strategies, and interference constraints [10]. This study aims to mitigate CCI by designing optimal resource allocation strategies that include channel selection and power control. Optimization theory and heuristic algorithms are two main approaches used to model and solve resource allocation problems in CRNs. For example, in [11], the authors conducted a study on optimizing energy-efficient resource distribution in CRNs employing Orthogonal Frequency Division Multiplexing (OFDM) technology by formulating a non-linear fractional programming problem, which was solved using a time-sharing approach to achieve near-optimal solutions.