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Bayesian Inference for Localization in Cellular Networks.

Abstract : In this paper, we present a general technique based on Bayesian inference to locate mobiles in cellular networks. We study the problem of localizing users in a cellular network for calls with information regarding only one base station and hence triangulation or trilateration cannot be performed. In our call data records, this happens more than 50% of time. We show how to localize mobiles based on our knowledge of the network layout and how to incorporate additional information such as round-trip-time and signal to noise and interference ratio (SINR) measurements. We study important parameters used in this Bayesian method through mining call data records and matching GPS records and obtain their distribution or typical values. We validate our localization technique in a commercial network with a few thousand emergency calls. The results show that the Bayesian method can reduce the localization error by 20% compared to a blind approach and the accuracy of localization can be further improved by refining the a priori user distribution in the Bayesian technique
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Contributor : Fabien Mathieu Connect in order to contact the contributor
Submitted on : Saturday, February 1, 2014 - 6:44:47 PM
Last modification on : Friday, November 18, 2022 - 9:26:48 AM


  • HAL Id : hal-00940550, version 1


Hui Zang, François Baccelli, Jean Bolot. Bayesian Inference for Localization in Cellular Networks.. INFOCOM 2010 - 29th IEEE International Conference on Computer Communications, Mar 2010, San Diego, CA, United States. pp.1963-1971. ⟨hal-00940550⟩



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