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In a wireless network, gathering information at the base station about mobileusers based only on uplink channel measurements is an interesting challenge.Indeed, accessing the users locations and predicting their downlink channelswould be particularly useful in order to optimize the network efficiency. Inthis paper, a supervised machine learning approach addressing these tasks in anunified way is proposed. It relies on a labeled database that can be acquiredin a simple way by the base station while operating. The proposed regressionmethod can be seen as a computationally efficient two layers neural networkinitialized with a non-parametric estimator. It is illustrated on realisticchannel data, both for the positioning and channel mapping tasks, achievingbetter results than previously proposed approaches, at a lower cost.

Similarity-based prediction for channel mapping and user positioning