4G Data Analysis and Downlink Throughput Predictive Modelling Around The Geographic Region of Gazipur and Dhaka

Abstract

This project presents a comprehensive study on optimizing 4G data analysis and downlink throughput modelling, integrating technical rigor with societal considerations. As 4G networks continue to serve as a backbone for modern telecommunications, enhancing their efficiency and performance is crucial for meeting growing data demands and supporting emerging applications. This project aims to develop advanced models and algorithms that improve network performance while addressing broader impacts on safety and societal aspects. The methodology involves the use of diverse resources, including datasets that were collected by hand from selected regions of interests in Gazipur and Dhaka, specialized software tools for model simulation, data analysis and network performance data collection, and case studies of real-world 4G network deployments. Advanced statistical methods, machine learning algorithms, and optimization techniques are employed to analyze data and model throughput. The project tries to emphasize the importance of data anonymization, security, and compliance with data protection regulations in an attempt to address ethical and privacy concerns. The results demonstrate that machine learning models are able to simulate and predict network downlink throughput with acceptable standards of accuracy, validated through rigorous analysis and evaluation. Detailed data analysis reveals patterns and trends that inform the optimization models, while comparative analysis with existing studies highlights the advancements achieved. In addition, this project underscores the role of engineering in society, addressing the ethical and societal implications of 4G technology. The findings contribute to the technical field of telecommunications while promoting sustainable and inclusive connectivity solutions.