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Similarity-based prediction for channel mapping and user positioning

Abstract : In a wireless network, gathering information at the base station about mobile users based only on uplink channel measurements is an interesting challenge. Indeed, accessing the users locations and predicting their downlink channels would be particularly useful in order to optimize the network efficiency. In this paper, a supervised machine learning approach addressing these tasks in an unified way is proposed. It relies on a labeled database that can be acquired in a simple way by the base station while operating. The proposed regression method can be seen as a computationally efficient two layers neural network initialized with a non-parametric estimator. It is illustrated on realistic channel data, both for the positioning and channel mapping tasks, achieving better results than previously proposed approaches, at a lower cost.
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Contributor : Luc Le Magoarou <>
Submitted on : Tuesday, January 5, 2021 - 10:57:12 AM
Last modification on : Friday, January 15, 2021 - 3:19:52 AM


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  • HAL Id : hal-02974852, version 3
  • ARXIV : 2101.05217



Luc Le Magoarou. Similarity-based prediction for channel mapping and user positioning. IEEE Communications Letters, Institute of Electrical and Electronics Engineers, inPress. ⟨hal-02974852v3⟩



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