Mobile Station Location using Genetic Algorithm Optimized Radio Frequency Fingerprinting
Rafael Saraiva Campos, Lisandro Lovisolo

DOI: 10.14209/sbrt.2010.5
Evento: VII International Telecommunications Symposium (ITS2010)
Keywords: Mobile Station Location Radio Frequency Fingerprints Propagation Modeling Genetic Algorithms First Generation Population Correlation Space
Radio Frequency Fingerprinting estimates the mobile station location by comparing a measured radio frequency fingerprint with a set of previously collected or generated reference fingerprints. This set is referred to as the search or correlation space. Genetic algorithms can be used to optimize both the location accuracy and the time required to produce a position fix, reducing the size of the search space. This paper proposes an innovation in such application of genetic algorithms, restricting the first generation population to the predicted best server area of the serving sector measured by the mobile station. In field tests in a GSM cellular network in a dense urban environment, this approach achieved reductions of 20% and 15% in the 50-th and 90-th percentile location errors, respectively, in comparison to the original formulation, where the initial population is randomly distributed throughout the whole service area. An average reduction of 91% in the time to produce a position fix was also observed