Similarity-based prediction for channel mapping and user positioning

TL;DR

This paper presents a supervised machine learning approach using a two-layer neural network for predicting user positions and downlink channels in wireless networks, based on uplink measurements, improving accuracy and efficiency.

Contribution

It introduces a unified, computationally efficient neural network method for channel mapping and user positioning using uplink data, outperforming previous approaches.

Findings

Achieves better accuracy than prior methods

Operates at lower computational cost

Validated on realistic channel data

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.