## 2 Related Work  
Various current investigations have tackled the problem of mode selection, channel assignment, and power allocation for D2D communication underlay cellular networks. For instance, in [13], the resource allocation problem has been formulated based on the investigation of the system model in many-to-many matching D2D communication underlying cellular networks. An overlapping coalition game method that utilizes a candidate sequence is introduced to improve the D2D transmission data rate. The simulation findings showed that the efficiency of the proposed method, which incorporates an optimization technique for candidate sequences, outperforms that of the overlapping approach with random optimization. In [14], a genetic algorithm-based joined power and channel allocation was investigated in D2D-underlayed cellular networks. Efficient allocation of transmitted power and shared channels for every user is achieved by a genetic algorithm optimization, that assists in minimizing interference. The simulation outcomes illustrated that the proposed approach outperformed the fixed, random, and particle swarm optimization approaches in terms of maximizing the total resource utilization. In [15], the authors addressed the issues of mode selection, spectrum usage, and power management in D2D underlay cellular communications by the use of a hierarchical game approach. For mode selection and channel assignment issues, the optimal solution is achieved by employing the hedonic coalition game. Moreover, the non-cooperative game is employed to tackle the issue of power management in the proposed scheme. The algorithm effectively addressed the issue of fair allocation of resources among users, while simultaneously improving the system throughput. In [16], the interference management issue was investigated in order to effectively enable the coexistence of two technologies: a massive MIMO and a D2D communication system that shares uplink network resources under cellular networks. To achieve a distributed solution, they formulated the problem via a matching theory and presented a resource optimization technique utilizing the principles of many-to-many matching to enhance the performance of the system. The numerical outcomes illustrated that the suggested method effectively improved the network performance by leveraging the diversification benefits of massive MIMO and matching users based on their preferences. Moreover, in [17], the paper presented a dynamic resource allocation strategy for D2D communications under cellular networks. The suggested resource allocation technique incorporates a Q-learning-based power management algorithm to allocate optimum powers for D2D pairs, with the goal of optimizing the throughput. The introduced algorithm employs a Q-learning technique to enhance the transmission power for the D2D pairs that utilize the same resource block at the establishment of a unique D2D pair. The model findings showed that the suggestedtechnique can achieve quick convergence and outperform the random power allocation algorithm in terms of overall throughput. In [18], the authors examined techniques for allocating resources in D2D communications underlaying cellular networks. A unique DC programming method for the difference of convex functions is provided to successfully address this complex resource allocation problem. The simulation findings showed that the introduced system reaches the maximum weighted sum-data rate in comparison to the benchmark methods, while simultaneously guaranteeing the satisfaction of QoS demands for each D2D pair and CUs. Furthermore, in [19], the quality of cell-edge user coverage was enhanced with the development of a D2D-relay communication system for underlay cellular networks. Subsequently, the Lagrange dual approach-based power allocation technique was developed to effectively allocate power levels to the users in the network. The suggested technique converges in time, and an optimal closed-form solution is derived.