Locating eNodeBs through sectorization inference—Sector fitting evaluated on a railway use case

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The ability to locate a radio transmitter can be useful in many contexts, and a range of localization methods have been proposed. In the context of cellular networks, the position of the base station is known to the operator and regulator, but often this knowledge is not publicly available. For the problem of base station localization, several approaches have been examined in the literature, and several public services exists which estimate the position of cellular infrastructure based on measurement data collected from cellular users. In this work we present sector fitting, a new approach for locating sectorized transmitters based only on observations of the positions and sector identifiers as reported by the cellular UEs. Sector fitting defines a sectorization model which is applied over a search grid to obtain a cost matrix, which is then merged over multiple frequencies to arrive at the best base station location estimate. An extensive evaluation of sector fitting is carried out, using a large data set of observations from train-mounted LTE modems. The results show that sector fitting outperforms the other applicable localization methods. Furthermore, an iterative grid search approach is examined and demonstrated to achieve the same localization accuracy as a full search while drastically reducing the computational cost. Finally, three downsampling methods are evaluated with the results showing that a trade-off can be made to further reduce computational cost, but with slightly worse localization accuracy in most cases.