## 2 Related Work
They introduced a resource allocation algorithm based on the relationship between the distance of the users and the interference levels to assign channel resources efficiently. This technique enables D2D pairs to efficiently reuse channel resources utilized by CUs in close proximity while minimizing interference between D2D pairs and CUs. Moreover, the particle swarm optimization technique was employed to tackle the optimum power allocation problem, aiming to obtain the highest transmission rate of the network. The simulation findings demonstrated that this approach enhanced system data rate and performance, while simultaneously minimizing the computational complexity. In [26], a resource allocation technique is suggested, based on the QL algorithm, for D2D communication in the unlicensed spectrum. This approach allows for dynamic allocation of transmitted power to D2D users based on updating network traffic conditions, leading to improved performance of the coexisting system. The agent's states are determined by different factors such as fairness, SNR, and data rate of CUs. The agent's actions can be determined by the different duty cycles and transmission power levels. The agent can learn the duty cycles and optimum power transmission via iterative interaction responses to the network. The comprehensive simulation findings indicated that the proposed technique outperforms the compared approaches in resource allocation fairness and throughput. The authors in [27] tackled a joint optimization issue of resource allocation, optimum power management, and relay-selection in a two-way relaying approach underlaying cellular networks. The power management problem is solved using particle swarm optimization, while the relay selection problem is addressed via the one-to-one stable matching technique. The numerical outcomes indicated that employing stable matching in the relay selection problem significantly increased the performance of the network, D2D data rate, and EE. In [28], the authors investigated the enhancement of system fairness and throughput in an underlying D2D communications scheme by employing joint channel allocation and power management methodologies. They suggested an iterative resource allocation approach based on RL by considering the channel parameters. The authors proposed an enhanced reinforcement learning-based SARSA algorithm. The simulation findings illustrated the enhancement of the system's throughput, EE, and SE. In [29], the authors examined EE optimization for D2D communications underlaying cellular networks. The primary objective of the study is to improve the performance of the scheme as well as reduce the power conception of the equipment while maintaining the QoS for every user. The Q-learning algorithm is applied to achieve optimum communication between the users and gNB in the proposed network. The simulation findings demonstrated that the proposed system enhanced the transmission performance as compared with the traditional systems. In [30], the authors proposed a joint channel allocation and power allocation issue for D2D connections within cellular networks. The main goal of this research is to optimize the EE of the proposed scheme while satisfying the QoS of CUs and D2D communications. The suggested problem is NP-hard and complex to solve; therefore, an iterative solution is introduced. The authors introduced [PERSON]'s method to address the problem and obtain the optimal solution. The simulation findings showed the advantage of the suggested technique relative to conventional schemes.