Spatial Interpolation based Cellular Coverage Prediction with Crowdsourced Measurements

Massimiliano Molinari, Mah-Rukh Fida, Mahesh Marina, Antonio Pescape

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Coverage prediction has always been of great concern for mobile network operators. Yet the prevalent approach using analytical models assisted by drive testing based measurements is inherently inaccurate and expensive. We consider a promising alternative for coverage mapping involving crowdsourced measurements and spatial interpolation. In particular, we empirically study the accuracy of wide range of spatial interpolation techniques in different scenarios that capture the unique characteristics of crowdsourced measurements (inaccurate locations, sparse and non-uniform measurements, etc.), and find ordinary kriging to be a fairly robust technique.
Original languageEnglish
Title of host publicationACM SIGCOMM Workshop on Crowdsourcing and crowdsharing of Big (Internet) Data (C2B(I)D)
Subtitle of host publicationCo-located with ACM SIGCOMM’ 15
Number of pages6
Publication statusPublished - 17 Aug 2015


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