Deep Learning-based Symbolic Indoor Positioning using the Serving eNodeB

TL;DR

This paper introduces a deep learning-based indoor positioning system that leverages cellular signals from a serving eNodeB and uses Denoising Autoencoders to improve accuracy, eliminating the need for specialized infrastructure.

Contribution

It proposes a novel indoor positioning approach using cellular signals and autoencoders, with real-world validation and publicly available data and code.

Findings

Outperforms traditional symbolic indoor positioning methods

Effective mitigation of cellular signal loss

Validated with real-world smartphone data

Abstract

This paper presents a novel indoor positioning method designed for residential apartments. The proposed method makes use of cellular signals emitting from a serving eNodeB which eliminates the need for specialized positioning infrastructure. Additionally, it utilizes Denoising Autoencoders to mitigate the effects of cellular signal loss. We evaluated the proposed method using real-world data collected from two different smartphones inside a representative apartment of eight symbolic spaces. Experimental results verify that the proposed method outperforms conventional symbolic indoor positioning techniques in various performance metrics. To promote reproducibility and foster new research efforts, we made all the data and codes associated with this work publicly available.