Energy Efficient Device to Device Data Transmission Based on Deep Artificial Learning in 6G Networks

Abstract

– The rising wireless service constraints and user compactness have to lead the progress of 6G communication in the modern days. The benefit of 6G over the presented technologies is a huge support for mixed applications and mobility maintenance. Device to Device (D2D) data transmission in 6G has great attention since it gives a better data delivery rate (DDR). Recently, several methods were established for D2D data transmission. However, energy consumption was not considered to improve the network throughput. To handle such problems, an artificial intelligence technique called Deep Neural Regressive Tangent Transfer Classifier (DNRTTC) model is introduced in this research for D2D data transmission in a 6G system. The designed method includes several layers to attain energy-efficient D2D data transmission. The primary layer is the input layer and it includes several mobile nodes as input. Nodes are transmitted to the hidden layer one. For each node, energy, received signal strength, and connection speed of each mobile node is calculated. Then the similarity analysis is done in the following layer where each node is analyzed with its threshold value. The result is sent to the output layer where the better resource mobile nodes are identified by using the activation function. This leads to attaining energy-efficient D2D data transmission in 6G. Results illustrate that the DNRTTC outperformed compared to conventional methods with better energy efficiency, packet delivery ratio, throughput.