Indoor positioning based on RSS fingerprinting in a LTE Network: Method based on genetic algorithms

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    7 Citations (Scopus)

    Abstract

    We study the performance of a composite positioning method based on Genetic Algorithms (GA) for determining the position of an indoor user using Radio Signal Strength (RSS) fingerprinting. RSS samples are obtained from field measurements in a test-bed with a dedicated LTE network. Radio maps are described by functions obtained by symbolic regression. The position of a user is then obtained by solving an optimization problem with the functions and instantaneous measured RSS values. The proposed method is benchmarked against a solution implemented with a Neural Network. Position accuracy is evaluated in scenarios with different characteristics. Conclusions are drawn about performance and complexity of the assessed methods.

    Original languageEnglish
    Title of host publication2019 IEEE International Conference on Communications Workshops, ICC Workshops 2019 - Proceedings
    PublisherIEEE Institute of Electrical and Electronic Engineers
    Number of pages6
    ISBN (Electronic)978-1-7281-2373-8
    DOIs
    Publication statusPublished - May 2019
    MoE publication typeA4 Article in a conference publication
    Event2019 IEEE International Conference on Communications Workshops: ICC Workshops 2019 - Shanghai, China
    Duration: 20 May 201924 May 2019

    Workshop

    Workshop2019 IEEE International Conference on Communications Workshops
    Country/TerritoryChina
    CityShanghai
    Period20/05/1924/05/19

    Keywords

    • Genetic Algorithm
    • Indoor positioning
    • LTE
    • Neural Network
    • RSS fingerprinting
    • Symbolic Regression

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