Maximum likelihood geolocation in LTE cellular networks using the timing advance parameter

Roth, John D.; Tummala, Murali; McEachen, John; Scrofani, James; DeGabriele, Robert A.

doi:10.1109/icspcs.2016.7843379

Cited by 9 publications

(2 citation statements)

References 18 publications

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“…Likewise, a stochastic scheme that can identify and discard non-line-of sight (NLOS) measurements is proposed in [38]. For their TA based UE localization scheme, the authors in [18] extend this notion by explicitly accounting for three different error sources that distort the reported travelled distance between UE and BS. As such, they introduce generic Gaussian measurement noise and a dedicated quantization term modeled as a Gaussian or uniform distribution.…”

Section: Related Workmentioning

confidence: 99%

“…With detailed network topology data, researchers can assess potential challenges and risks for novel applications before deployment. Clearly, also UE positioning itself benefits from improved BS localization: In a bootstrap manner, we can use noisy UE position estimates to determine BS locations, which can in turn act as a basis for the positioning of UEs whenever Global Navigation Satellite systems (GNSSs) are unavailable [18]. Overall, we identify two recent developments that render such detailed inference of the network topology feasible.…”

Section: Introductionmentioning

confidence: 99%

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Localizing Basestations From End-User Timing Advance Measurements

et al. 2022

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Although mobile communication has become ubiquitous in our modern society, operators typically treat the underlying networking infrastructure in a secretive manner. However, detailed topology information is a key enabler for operator benchmarking and can serve as ground-truth data for system-level simulations or user equipment localization schemes. Still, existing approaches for base station localization that are either based on received signal strength or on the spatial distribution of measurements are not accurate enough for such use cases. Accordingly, we propose a localization scheme that operates on end-user measurements of the 4G & 5G timing advance parameter, which acts as a quantized distance measure between the user equipment and the base station. By directly incorporating GPS noise, multipath propagation, and quantization into our stochastic system model, we obtain an estimator that offers a reliable measure of confidence and requires only the configuration of two hyperparameters with a dedicated physical interpretation. We evaluate our approach using a set of drive-test measurements consisting of 190 LTE eNodeBs with ground-truth locations confirmed by an Austrian mobile network operator. Our selective estimator can either operate without prior knowledge, resulting in mean distance errors of below 100 m, or in a classification setup, where it correctly identifies up to 95% of eNodeBs from a set of candidate cell tower locations. To allow for reproducibility, we make our dataset and a reference implementation publicly available.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%