Similarity-Based Prediction for Channel Mapping and User Positioning

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 locations and predicting their downlink channels would be particularly useful in order to optimize network efficiency. this letter, supervised machine learning approach addressing these tasks unified way proposed. It relies labeled database that can acquired simple by while operating. The proposed regression method seen as computationally efficient two layers neural initialized with non-parametric estimator. illustrated realistic data, both for positioning mapping tasks, achieving better results than previously approaches, lower cost.