Open access
Date
2022Type
- Conference Paper
ETH Bibliography
yes
Altmetrics
Abstract
We consider a scalable User Equipment (UE)-side indoor localization framework that processes Channel State Information (CSI) from multiple Access Points (APs). We use CSI features that are resilient to synchronization errors and other hardware impairments. As a consequence our method does not require accurate network synchronization among APs. Increasing the number of APs considered by a UE profoundly improves fingerprint positioning, with the cost of increasing complexity and channel estimation time. In order to improve scalability of the framework to large networks consisting of multiple APs in many rooms, we train a multi-layer neural network that combines CSI features and unique AP identifiers of a subset of APs in range of a UE. We simulate UE-side localization using CSI obtained from a commercial raytracer. The considered method processing frequency selective CSI achieves an average positioning error of 60cm, outperforming methods that process received signal strength information only. The mean localization accuracy loss compared to a non-scalable approach with perfect synchronization and CSI is 20cm. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000580771Publication status
publishedExternal links
Book title
2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)Pages / Article No.
Publisher
IEEEEvent
Organisational unit
09695 - Studer, Christoph / Studer, Christoph
Funding
813999 - Integrating wireless communication engineering and machine learning (EC)
Notes
Conference lecture held on July 5, 2022More
Show all metadata
ETH Bibliography
yes
Altmetrics