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Open AccessArticle

Big Data-Driven Cellular Information Detection and Coverage Identification

by Hai Wang 1, Su Xie 1, Ke Li 1,* and M. Omair Ahmad 2
1
College of Smart City, Beijing Union University, Beijing 100101, China
2
Department of Electrical and Computer Engineering, Concordia University, Montreal, QC H3G IM8, Canada
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(4), 937; https://doi.org/10.3390/s19040937
Received: 23 December 2018 / Revised: 11 February 2019 / Accepted: 15 February 2019 / Published: 22 February 2019
(This article belongs to the Special Issue Mobile Sensing: Platforms, Technologies and Challenges)
As one of the core data assets of telecom operators, base station almanac (BSA) plays an important role in the operation and maintenance of mobile networks. It is also an important source of data for the location-based service (LBS) providers. However, it is always less timely updated, nor it is accurate enough. Besides, it is not open to third parties. Conventional methods detect only the location of the base station (BS) which cannot satisfy the needs of network optimization and maintenance. Because of these drawbacks, in this paper, a big-data driven method of BSA information detection and cellular coverage identification is proposed. With the help of network-related data crowd sensed from the massive number of smartphone users in the live network, the algorithm can estimate more parameters of BSA with higher accuracy than conventional methods. The coverage capability of each cell was also identified in a granularity of small geographical grids. Computational results validate the proposed algorithm with higher performance and detection ability over the existing ones. The new method can be expected to improve the scope, accuracy, and timeliness of BSA, serving for wireless network optimization and maintenance as well as LBS service. View Full-Text
Keywords: base station almanac; data mining; mobile crowdsensing; network measurement base station almanac; data mining; mobile crowdsensing; network measurement
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Wang, H.; Xie, S.; Li, K.; Ahmad, M.O. Big Data-Driven Cellular Information Detection and Coverage Identification. Sensors 2019, 19, 937.

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