With millimeter wave wireless communications, the resulting radiation reflects on most visible objects, creating rich multi path environments, namely in urban scenarios. The radiation captured by a listening device is thus shaped by the obstacles encountered, which carry latent information regarding their relative positions. In this paper, a system to convert the received millimeter wave radiation into the device's position is proposed, making use of the aforementioned hidden information. Using deep learning techniques and a pre-established codebook of beamforming patterns transmitted by a base station, the simulations show that average estimation errors below 10 meters are achievable in realistic outdoors scenarios that contain mostly non-line-of-sight positions, paving the way for new positioning systems.


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    Title :

    Beamformed Fingerprint Learning for Accurate Millimeter Wave Positioning


    Contributors:


    Publication date :

    2018-08-01


    Size :

    872130 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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