We define covariance difference of arrival (CDOA) features derived from channel state information that can be used for machine learning based fingerprinting localization in non-line of sight (NLoS) conditions, with minimal communication overhead. Taking advantage of the uniqueness of the multipath channel between the base station (BS) and user equipment (UE) at different locations in the geographical region of interest. UEs compute CDOA features, consisting of pair-wise distances between covariance matrices of received signals from multiple BSs. Measured features are fed back to the network, where fingerprinting localization is performed. We consider both k-nearest neighbour and neural network localization, and investigate the trade-off between localization performance and communication overhead. In simulations of a NLoS 5G NR factory scenario with eight-antenna BSs, CDOA features provide a localization error less than 0.91 m in 80% of the cases, as compared to 0.78 m for a benchmark method where UEs feed back complete measured covariance matrices to the network, and 1.36 m for power difference of arrival features. Comparing to complete covariance feedback, CDOA features reduce communication overhead by 98%.


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    Title :

    Covariance Difference of Arrival based Fingerprinting Localization


    Contributors:


    Publication date :

    2023-06-01


    Size :

    1476552 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English





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