Neural Positioning Without External Reference

TL;DR

This paper introduces a neural positioning method that trains without external reference systems by using CSI data and relative displacement commands on COTS robots, enabling accurate UE localization over large areas.

Contribution

The work presents a novel neural positioning pipeline that eliminates the need for costly external reference systems by training solely with CSI and robot displacement data.

Findings

Achieves localization accuracy close to state-of-the-art methods.

Effective in both LoS and NLoS environments.

Validated with Wi-Fi and 5G NR CSI measurements.

Abstract

Channel state information (CSI)-based user equipment (UE) positioning with neural networks -- referred to as neural positioning -- is a promising approach for accurate off-device UE localization. Most existing methods train their neural networks with ground-truth position labels obtained from external reference positioning systems, which requires costly hardware and renders label acquisition difficult in large areas. In this work, we propose a novel neural positioning pipeline that avoids the need for any external reference positioning system. Our approach trains the positioning network only using CSI acquired off-device and relative displacement commands executed on commercial off-the-shelf (COTS) robot platforms, such as robotic vacuum cleaners -- such an approach enables inexpensive training of accurate neural positioning functions over large areas. We evaluate our method in three real-world scenarios, ranging from small line-of-sight (LoS) areas to larger non-line-of-sight (NLoS) environments, using CSI measurements acquired in IEEE 802.11 Wi-Fi and 5G New Radio (NR) systems. Our experiments demonstrate that the proposed neural positioning pipeline achieves UE localization accuracies close to state-of-the-art methods that require externally acquired high-precision ground-truth position labels for training.