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

The problem of locating radio devices has been addressed by a variety of methods. In the cellular setting, most of the focus have been on locating user equipment (UE). This work focuses on the inverse problem, i.e. locating the eNodeB based on received signal strength (RSS) measurements collected by UEs. We perform a comprehensive evaluation of six variations of two RSS-modeling based localization approaches. Furthermore, two methods for fusing the location estimates of the individual cells were also examined. The evaluation is done using a manually created ground truth data set for eNodeB positions, and a large measurement data set comprising of more than four million observations collected from cellular modems onboard Swedish trains. The best localization accuracy was obtained by one of our proposed variations of logloss fitting using geographic aggregation with highest mean RSRP as the reference point selection criteria. When combined with centroid-based fusion of the individual cell estimates, a median eNodeB localization error of 433 m was obtained, which is a considerable improvement over the second-best approach which achieved a median error of 674 m. The centroid-based fusion approach was found to consistently outperform the DPD fusion approach, which in turn had a better localization error distribution than obtained for solitary cells.HITS, 470