Abstract:
Telco technologies have grown rapidly from 2G to 5G. Telco operators manage, operate, and optimize networks with precise outdoor localization. Telco operators use MR data to localize outdoor mobile devices, unlike GPS.
Telco localization errors are high when given MR samples with noisy signals (e.g., Telco signal interference and attenuation). This paper discusses algorithms to detect and repair outlier positions with high errors to improve Telco localization accuracy.
RLoc is a context-aware Telco localization technique that uses a machine-learning-based localization algorithm, a detection algorithm to find flawed samples, and a repair algorithm to replace outlier localization results with better ones (ideally ground truth positions).
We exploit trajectory context and spatio-temporal locality of MR locations to detect and repair flawed positions. Our real MR data sets from 2G GSM and 4G LTE Telco networks show that RLoc can greatly improve Telco location accuracy. RLoc on a large 4G MR data set achieves 32.2 meters of median errors, 17.4% better than state-of-the-art.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
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