An Efficient Resource Allocation Mechanism with Fuzzy C-Means and Adaptive RNNs for D2D Communications in Cellular Networks

Direct communication links between nearby users can be established via device-to-device (D2D) communications, eliminating the need for a base station (BS) or remaining core networks. The D2D users’ transmission power is lower than the BS’s traffic burden. Nonorthogonal multiple access (NOMA) expertise allows a transmitter to direct multiple impulses at the same wavelength by power superposition, possibly enhancing spectrum efficiency. In this work, an adaptive recurrent neural network (ARNN) is developed to effectively handle the nonlinearity of transmission powers and channel diversity. Furthermore, a method called fuzzy C-means clustering (FCMC) is designed to group users on different subcarriers with different strengths. For spectrum utilization to improve, clustering is necessary. The advanced coati optimization algorithm (ACOA) is subsequently utilized to assign assets. The Levy Flight (LF) function is taken into consideration when choosing the weight value in the Coati Optimization Algorithm (COA). The simulation findings demonstrate that our method is better at increasing system throughput while meeting users’ file requests. This method enables the efficient use of resources and power control in interactions between devices. The proposed method is implemented in MATLAB, and its performance is evaluated via performance measures. It is compared with conventional approaches. The results indicate that the suggested method achieves superior outage probability values across different user counts, with values of 0.99465 for 40 users, 0.99946 for 60 users, 0.99946 for 80 users, and 0.999446 for 100 users. Comparatively, the Recurrent Neural Network- Honey Badger Algorithm (RNN-HBA) achieved slightly lower outage probabilities, whereas the Deep Belief Network (DBN) and Particle Swarm Optimization (PSO) demonstrated more significant variations, especially with a greater number of users.