Deep Learning-based Symbolic Indoor Positioning using the Serving eNodeB

Alhomayani, Fahad; Mahoor, Mohammad H.

doi:10.1109/icmla51294.2020.00083

Cited by 2 publications

(1 citation statement)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the other hand, the fourth system is a cellular-based localization system. There are plenty of localization systems that depend on cellular signals for both indoor and outdoor environments [23]- [31]. Fingerprint-based localization systems enjoy several strengths that enable them to yield better performance.…”

Section: Introductionmentioning

confidence: 99%

DeepFeat: Robust Large-Scale Multi-Features Outdoor Localization in LTE Networks Using Deep Learning

Tharwat²,

Magdy³

et al. 2022

IEEE Access

View full text Add to dashboard Cite

Location-based services in different applications push the research toward outdoor localization for users' equipment in Long Term Evolution (LTE) networks. Telecom operators can introduce valuable services to users based on their location, both in emergency and ordinary situations. This paper introduces DeepFeat: A deep-learning-based framework for outdoor localization using a rich feature set in LTE networks. DeepFeat works on the mobile operator side, and it leverages many mobile network features and other metrics to achieve high localization accuracy. In order to reduce computation and complexity, we introduce a feature selection module to choose the most appropriate features as inputs to the deep learning model. This module reduces the computation and complexity by around 20.6%, with enhancement in system accuracy. The feature selection module uses correlation and Chi-squared algorithms to reduce the feature set to 12 inputs only regardless of the area size, compared to a large number of cell towers in similar systems; such input increases exponentially with increasing the test area. In order to enhance the accuracy of DeepFeat, a One-to-Many augmenter is introduced to extend the dataset and improve the system's overall performance. The results show the impact of the proper features selection adopted by DeepFeat on the system performance. DeepFeat achieved median localization accuracy of 13.179m in an outdoor environment in a mid-scale area of 6.27Km 2 . In a large-scale area of 45Km 2 , the median localization accuracy is 13.7m. DeepFeat was compared to other state-of-the-art deep-learningbased localization systems that leverage a small number of features. We show that using the DeepFeat carefully selected feature set enhances the localization accuracy compared to the state-of-the-art systems by at least 286%.

show abstract

Section: Introductionmentioning

confidence: 99%