AI-Driven Regression Techniques for Modeling Mobile Network Technology (6G) Using TensorFlow-Keras Framework
Year: 2025
Type: article
Abstract: The advent of 6G wireless communication necessitates advanced modeling techniques to optimize network performance and efficiency for emerging applications such as the Internet of Things (IoT), augmented and virtual reality (AR/VR), and autonomous networks. Traditional simulation and optimization methods often prove inadequate for the complex and dynamic nature of these advanced systems, underscoring a critical need for sophisticated, data-driven approaches. This paper explores the application of Artificial Intelligence (AI)-driven regression techniques, implemented using the TensorFlow-Keras framework, for modeling key aspects of mobile network technology in the context of 6G. Drawing inspiration from the potential of generative AI in enhancing network simulations and optimizing RF communications, this research focuses on developing predictive models capable of accurately simulating the complex relationships within 6G systems. Our methodology involves leveraging Keras to build and train a series of regression models on relevant datasets to predict critical network parameters.
DOI: 10.61643/c45684
Authors: Musa Husseini, Upendra Kanuru
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7081970438