An Improved Positioning System for 6G Cellular Network

Abstract

Information about the whereabouts of oneself or our cell phones has always been crucial to humans. Technological advancements have supported us across various domains, spanning from interior environments to outdoor global positioning systems. However, achieving real-time indoor positioning has remained a formidable task. The requirement for enhanced positioning has gained prominence in the context of the emergence of 6G cellular networks. The introduction of novel radio technologies, characterized by reduced end-to-end latency, specialized control protocols, and increased processing capacity at the network edge, has opened doors to harnessing the 6G cellular network’s full potential for precise localization in indoor and outdoor settings. Within the 6G cellular network context, this study successfully implemented the classic signal fingerprinting approach using the Received Signal Strength Indicator in a combination with machine learning algorithms. Consequently, it proposed an improved positioning strategy designed for indoor localization scenarios in the next 6G cellular network environment. Using this method, with two datasets, the mean error distance of 0.08025523, meaning an accuracy of 99.92% and 0.10692412 was achieved, which is 99.89% accuracy respectively.