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L. Eller, P. Svoboda, M. Rupp:
"Bayesian Inference of Sector Orientation in LTE Networks based on End-User Measurements";
Talk: 2021 IEEE 94rd Vehicular Technology Conference (VTC2021-Fall), Online Only; 10-27-2021 - 10-28-2021; in: "Proc. 94rd IEEE Vehicular Technology Conference", (2021).

Access to reliable public data about the networking topology in a given area is an essential requirement for applications such as end-user localization; likewise, the subject is a significant area of research. For one, results from eNodeB localization can act as ground truth data for system-level simulations; for another, they can benefit the evaluation of advanced pathloss models. Although the orientation of a given sector antenna plays a crucial role for such use-cases, this aspect of network topology inference has not been prominently addressed in the literature. As such, we propose a Bayesian scheme operating on end-user \acrlong{rsrp} measurements from live LTE networks, to infer the orientation of the serving sector antenna. Hereby, we rely on a state-of-the-art 3GPP system model, with the sector orientation as the primary free parameter. The stochastic treatment allows us to directly incorporate the shadow-fading statistics into the estimation scheme, thus actively addressing the main limitations of \acrlong{rss}-based approaches. We examine our approach on a set of simulations before validating it on an extensive drive-test data set collected in Vienna, Austria, with ground truth orientations confirmed by a major Austrian \acrlong{mno}. On this data set, our shadow-fading aware approach achieves a median absolute error of $18^\circ$, which can be further reduced to $11^\circ$ by incorporating the generic assumption of non-overlapping sectors. This shows, that end-user measurements are a suitable basis to expose this aspect of the network topology to researchers and industry observers.

Access to reliable public data about the networking topology in a given area is an essential requirement for applications such as end-user localization; likewise, the subject is a significant area of research. For one, results from eNodeB localization can act as ground truth data for system-level simulations; for another, they can benefit the evaluation of advanced pathloss models. Although the orientation of a given sector antenna plays a crucial role for such use-cases, this aspect of network topology inference has not been prominently addressed in the literature. As such, we propose a Bayesian scheme operating on end-user \acrlong{rsrp} measurements from live LTE networks, to infer the orientation of the serving sector antenna. Hereby, we rely on a state-of-the-art 3GPP system model, with the sector orientation as the primary free parameter. The stochastic treatment allows us to directly incorporate the shadow-fading statistics into the estimation scheme, thus actively addressing the main limitations of \acrlong{rss}-based approaches. We examine our approach on a set of simulations before validating it on an extensive drive-test data set collected in Vienna, Austria, with ground truth orientations confirmed by a major Austrian \acrlong{mno}. On this data set, our shadow-fading aware approach achieves a median absolute error of $18^\circ$, which can be further reduced to $11^\circ$ by incorporating the generic assumption of non-overlapping sectors. This shows, that end-user measurements are a suitable basis to expose this aspect of the network topology to researchers and industry observers.

antenna pattern, base station, Bayesian, crowdsourcing, eNodeB, localization, MCMC, RSRP, sector orientation