Context-Aware Telco Outdoor Localization

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.

Deterministic Localization for Fully Automatic Operation: A Survey and Experiments

“…In addition to communication applications, the flight time and strength of LTE signals can be measured for mobile target localization [36]. With multiple measured time and strengths from different LTE nodes, ToA/TDoA and fingerprint algorithms were utilized to solve the target's position, which could be enhanced by deep-learning or machine-learning frameworks [35,37]. Among them, ref.…”

“…Among them, ref. [37] proposed an outlier detection and correction algorithm, which exploited trajectory context. Compared with other localization technologies shown in Table 1, it has a lower cost and better coverage in both indoor and outdoor scenarios.…”

Indoor Positioning Based Ranging: A Review and Literature Survey of UWB and Li-Fi Technologies

“…To address these challenges in an indoor environment, a number of different solutions that make use of a variety of technologies have been proposed. Examples of these technologies include ultra-wideband (UWB) [7], ZigBee [8,9], radio frequency identification (RFID) [10], wireless fidelity (Wi-Fi) [11], Bluetooth [12], frequency modulation (FM) [13], and cellular networks (including LTE and 5G) [14][15][16], wearable devices, and inertial sensors [17]. In addition, some hybrid strategies combine the positive aspects of two or more technologies in order to improve the accuracy of indoor localization [18].…”