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

Abstract

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%.

Vaccinated, What Next? An Efficient Contact and Social Distance Tracing Based on Heterogeneous Telco Data

“…• Fingerprinting approach is challenging in 3G and 4G networks due to the reduction of the available cell information, which only includes the associated serving cell and sometimes the strongest neighboring cells [65], [66]. However, this problem exists only at the client side mode [67] and some solutions have been proposed to mitigate its effect, e.g., [11]. • Fingerprinting-based localization is expensive in terms of data collection and maintenance.…”

Novel LSTM-Based Approaches for Enhancing Outdoor Localization Accuracy in 4G Networks

“…• Our work introduces uni-directional and bi-directional LSTM deep learning models, leveraging temporal characteristics from the dataset utilized in DeepFeat [35]. These models exhibit superior accuracy, outperforming existing state-of-the-art models.…”

Deep Learning-based Fingerprinting for Outdoor UE Positioning Utilising Spatially Correlated RSSs of 5G Networks

“…Deep learning approaches have been utilised recently for both indoor and outdoor fingerprinting [8][9][10][11][12][13][14]. In [8] the authors demonstrated the correlation in the received signal strengths (RSS) in time and their impact on improving the localisation accuracy.…”

“…The authors in [10] used the RSSs from multiple beams as input to the fingerprinting multilayer perceptron (MLP). In [11] and [12], the authors extracted features from the signal and beams such as angle of beam departure, cell identity and channel bandwidth and combined them using an MLP.…”