A Novel RSS Fingerprint to Locate User Equipment UE in Remote Area | [International Journal of Membrane Science and Technology • 2024]

Author(s):

1. Mustafa Abbas Shober: Computer Sciences, Higher Institute for Doctorate in Science and Technology, Lebanese University, Beirut, Lebanon

2. Rami Tawil: Computer Sciences, Higher Institute for Doctorate in Science and Technology, Lebanese University, Beirut, Lebanon

Abstract:

Location determination algorithms are widely used in cellular networks, especially in the long-term evolution (LTE) network, to enable the provision of locationbased services (LBS). The increasing global demand for cellular networks has resulted in the creation of new user equipment (UE) positioning systems that align with the network's momentum. Regrettably, all of these technologies are hindered by their incapacity to ascertain the location of the UE in distant regions. This article introduces a novel method utilizing Radio Frequency (RF) fingerprinting to precisely locate UEs in remote areas. The approach entails employing a proposed partitioning model with a high level of precision, incorporating artificial intelligence and machine learning AI/ML in its fundamental state to reduce the search area. Furthermore, two algorithms are suggested: The first aims to enhance the efficiency of the battery with limited capacity by decreasing the frequency of measurements transmission. The second utilizes Jaccard similarity and incorporates the prefix filtering technique to determine matches. The algorithm is used to speed up the process of matching the fingerprint recorded in the fingerprint database with the fingerprint captured in real time. The results shows that it can reduces the transmission rate by 77.08% and achieves the lowest error rate of 35.34 m. Additionally, it exhibits a response time of 8 seconds.

Page(s): 467-486

DOI: 10.15379/ijmst.v11i1.3684

Published: Journal: International Journal of Membrane Science and Technology, Volume: 11, Issue: 1, Year: 2024

Keywords:

LTE , UE , LBS , RF Fingerprint , AIML

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