Context-aware Telco Outdoor Localization

TL;DR

This paper introduces RLoc, a context-aware machine learning approach for improving outdoor Telco localization accuracy by detecting and repairing noisy measurement reports using spatio-temporal information, significantly reducing median errors.

Contribution

The paper presents a novel context-aware localization method that leverages trajectory information to detect and repair outliers, outperforming existing techniques in accuracy.

Findings

RLoc achieves a median error of 32.2 meters on 4G data.

It improves localization accuracy by approximately 17.4% over state-of-the-art methods.

The approach effectively utilizes spatio-temporal context for outlier detection and repair.

Abstract

Recent years have witnessed the fast growth in telecommunication (Telco) techniques from 2G to upcoming 5G. Precise outdoor localization is important for Telco operators to manage, operate and optimize Telco networks. Differing from GPS, Telco localization is a technique employed by Telco operators to localize outdoor mobile devices by using measurement report (MR) data. When given MR samples containing noisy signals (e.g., caused by Telco signal interference and attenuation), Telco localization often suffers from high errors. To this end, the main focus of this paper is how to improve Telco localization accuracy via the algorithms to detect and repair outlier positions with high errors. Specifically, we propose a context-aware Telco localization technique, namely RLoc, which consists of three main components: a machine-learning-based localization algorithm, a detection algorithm to find flawed samples, and a repair algorithm to replace outlier localization results by better ones (ideally ground truth positions). Unlike most existing works to detect and repair every flawed MR sample independently, we instead take into account spatio-temporal locality of MR locations and exploit trajectory context to detect and repair flawed positions. Our experiments on the real MR data sets from 2G GSM and 4G LTE Telco networks verify that our work RLoc can greatly improve Telco location accuracy. For example, RLoc on a large 4G MR data set can achieve 32.2 meters of median errors, around 17.4% better than state-of-the-art.