Multi-Environment based Meta-Learning with CSI Fingerprints for Radio Based Positioning

TL;DR

This paper introduces a meta-learning approach for radio-based positioning using CSI fingerprints, enabling models to transfer knowledge across different environments and improve accuracy in new settings.

Contribution

It proposes a novel deep learning model with environment-independent features trained via meta-learning, enhancing transferability and positioning accuracy across multiple environments.

Findings

Meta-learned environment-independent features improve transfer learning.

The proposed method outperforms traditional transfer learning and training from scratch.

Enhanced positioning accuracy in new environments with fewer data.

Abstract

Radio based positioning of a user equipment (UE) based on deep learning (DL) methods using channel state information (CSI) fingerprints have shown promising results. DL models are able to capture complex properties embedded in the CSI about a particular environment and map UE's CSI to the UE's position. However, the CSI fingerprints and the DL models trained on such fingerprints are highly dependent on a particular propagation environment, which generally limits the transfer of knowledge of the DL models from one environment to another. In this paper, we propose a DL model consisting of two parts: the first part aims to learn environment independent features while the second part combines those features depending on the particular environment. To improve transfer learning, we propose a meta learning scheme for training the first part over multiple environments. We show that for positioning in a new environment, initializing a DL model with the meta learned environment independent function achieves higher UE positioning accuracy compared to regular transfer learning from one environment to the new environment, or compared to training the DL model from scratch with only fingerprints from the new environment. Our proposed scheme is able to create an environment independent function which can embed knowledge from multiple environments and more effectively learn from a new environment.