Carrier relevance study for indoor localization using GSM

A study is made of subsets of relevant GSM carriers for an indoor localization problem. A database was created containing power measurement scans of all available GSM carriers in 5 of 8 rooms of a second storey laboratory in central Paris, France, and a statistical learning algorithm developed to discriminate between rooms based on these carrier strengths. To optimize the system, carrier relevance was ranked using either Orthogonal Forward Regression or Support Vector Machine - Recursive Feature Elimination procedures, and a subset of relevant variables obtained with cross-validation. Results show that the 60 most relevant carriers are sufficient to correctly localize 97% of scans in an independent test set.