Indoor Sensing with Measurements

TL;DR

This paper explores indoor sensing using cellular channel perturbations, developing AI algorithms that detect presence with over 90% accuracy and estimate position within 0.7 meters, demonstrating the potential for indoor sensing applications.

Contribution

It introduces AI-based methods for indoor sensing using cellular channel measurements, including CNN and ensemble models, and demonstrates their effectiveness in presence detection and position estimation.

Findings

Presence detection accuracy exceeds 90% with simple CNN algorithms.

Position estimation achieves an average error of 0.7 meters with multiple links.

AI methods outperform baseline algorithms in indoor sensing tasks.

Abstract

The cellular wireless networks are evolving towards acquiring newer capabilities, such as sensing, which will support novel use cases and applications. Many of these require indoor sensing capabilities, which can be realized by exploiting the perturbation in the indoor channel. In this work, we conduct an indoor channel measurement campaign to study these perturbations and develop AI-based algorithms for estimating sensing parameters. We develop several AI methods based on CNN and tree-based ensemble architectures for sensing. We show that the presence of a passive target like a person can be detected from the channel perturbation of a single link with more than 90 % accuracy with a simple CNN based AI algorithm. However, sensing the position of a passive target is far more challenging requiring more complex AI algorithms and deployments. We show that the position of the human in the indoor room can be estimated within the average position error of 0.7 m with a deployment having three links and employing complex AI architecture for position estimation. We also compare the results with the baseline algorithm to demonstrate the utility of the proposed method.