eNB Location Estimation

“If you know the enemy and know yourself, you need not fear the result of a hundred battles. If you know yourself but not the enemy, for every victory gained you will also suffer a defeat. If you know neither the enemy nor yourself, you will succumb in every battle.”
― Sun Tzu, The Art of War

As said more than 2,500 years ago, there is no better way to win the competition against your rival networks but crystal-like knowing their network’s geographical topology.

Motiv Research recently developed an algorithm, which estimates the eNB locations of any LTE networks without having any pre-acquired information like antenna height or system parameter, etc. We utilizes Timing Advance received at UE but not limited to this data to estimate the eNB locations. Using the algorithm, we estimates how many sites, how many sectors and their azimuth antenna directions reliably. The estimated eNB location’s accuracy is within 100m even with few number of field data samples.

eNB location estimation is very useful mean for benchmarking itself but it can further advise operators where they need to install new sites such as small cells in cost-efficient manner to compete and win their rivals by identifying their sites locations. Moreover in the multi-layered LTE networks where Carrier Aggregation (CA) is recognized the key technology, the algorithm tells whether the competitor’s eNB sites of different RF layers are co-located or not so that how much CA can be effectively utilized in the area as CA can only be triggered between co-located primary cell and secondary cell. As a consequence, it is possible to estimate the CA performance of competitor’s network more accurately by taking into account if the multi-layers can really form CA or not and build your network deployment strategy to win subscribers on right time and right place.

The figure below shows an example of eNB location estimation using drive test data. The colors along the drive test route indicate PCI of LTE cells. When the number of samples of a cell is enough (> 100 samples), the accuracy of estimation is within 50m.

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