5G User Equipment (UE) Positioning and Localization Estimation using Machine Learning

The emergence of 5G technology has ushered in a new era of connectivity, making communications faster and cheaper. One of the main challenges of 5G networks is accuracy and efficiency, which are important for many applications such as driver less cars, smart cities and virtual reality. This research paper explores the integration of machine learning techniques to improve the accuracy and reliability of 5G infrastructure. The plan uses rich data generated by 5G networks, combining various parameters such as signal strength, flight time and angle of arrival. Machine learning algorithms are used to process this data and create good models for the correct location. The system is designed to adapt to dynamic and complex urban environments where traditional methods often encounter limitations. The main contribution of this research includes the development of a new machine learning-based distribution algorithm suitable for 5G networks, a comprehensive evaluation of performance comparison with the existing system, and evaluation of recommendations for feasibility and implementation. The results showed significant improvements in accuracy and reliability, demonstrating the potential of machine learning to revolutionize the 5G workplace.