The Enhanced Machine Learning Model for Device Prediction in Device-To-Device (D2D) Communications

Abstract

Device-to-Device (D2D) Communications is an emerging wireless technology which enables two or more devices to communicate with each other locally without the need for a base station or access point. In recent years, the number of networked devices has increased significantly, creating an ever-increasing demand for reliable and efficient communication solutions. To address this challenge, enhanced machine learning models have been developed for Device Prediction in D2D communications. These models use various supervised learning techniques such as deep learning, convolutional neural networks, and other important algorithms to identify the communication device and predict its visitation time and location. By taking into account factors such as user profiles, usage patterns, and vicinity environment, the model is then able to make predictions about the type of device that will connect to the communication network. By utilizing these models, the implementation of an efficient, low-overhead device prediction service can be achieved. Moreover, the application of this technology to many different networks and environments can strengthen network security and increase the reliability of communication.