Localization Performance for eNodeBs using Solitary and Fused RSS-Modeling Approaches

Sundberg, Simon; Garcia, Johan

doi:10.1109/wimob.2019.8923521

Cited by 4 publications

(2 citation statements)

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“…The most commonly found schemes in literature can be split into two categories: (i) approaches based on received signal strength (RSS) and (ii) geometric ones operating on the spatial distribution of measurements [24]. While RSS-based approaches have been successfully applied for related problems such as fingerprinting based small cell discovery [25], full localization schemes require accurate modelling of the expected path-loss over distance [26], [27]. This is especially tedious for urban environments with significant deviations due to shadow fading or blockages.…”

Section: Related Workmentioning

confidence: 99%

“…This is especially tedious for urban environments with significant deviations due to shadow fading or blockages. While fusing measurements from multiple sectors can improve the performance, the obtained mean positioning error is still in the range of 900 m [26]. Geometric methods, on the other hand, infer the eNodeB location indirectly from the cell assignment by operating on the spatial distribution of the measurements.…”

Section: Related Workmentioning

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: Related Workmentioning

confidence: 99%