Minimization of outage probability and energy consumption by deep learning-based prediction in D2D mm wave communication

Device-to-device (D2D) communication is used to frequently gather and exchange information in various domains. Millimeter-wave research has also incorporated D2D networks. The reliability of multiuser communication is more challenging because of the complex nature of wireless channels. In recent years, the supremacy of the D2D mm-wave communication model has been validated using the outage probability. Generally, the outage and minimize energy consumption to increase the robustness of the network coverage in the D2D mm-wave communication system. In this study, an optimization-enabled Deep Learning (DL) model is introduced to minimize the outage probability and energy consumption. Initially, the simulation of D2D communication was performed, and three types of D2D mm-wave communication coverage probability mechanisms, such as coherent, single-cluster approximation, and non-coherent lower bound, were considered. The minimization of the outage probability is performed using Flamingo Elk Herd Optimization (FEHO). Moreover, transit energy consumption is used to minimize the joint coverage probability by optimally devising a specific threshold. Here, a Deep Spiking Neural Network (DSNN) model is used to create a specific threshold for energy minimization. Furthermore, the performance of the FEHO+DSNN was evaluated by comparing it with existing techniques, where the proposed attained superior performance with 39.056 dBm, and 0.0015 for average transmit power and outage probability.