Accuracy-Aware Compression of Channel Impulse Responses using Deep Learning

Altstidl TR, Kram S, Herrmann O, Stahlke M, Feigl T, Mutschler C (2021)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2021

Publisher: IEEE Xplore

Pages Range: 1-8

Conference Proceedings Title: Proceedings of the International Conference on Indoor Positioning and Indoor Navigation (IPIN 2021)

Event location: Lloret de Mar ES

DOI: 10.1109/IPIN51156.2021.9662545

Abstract

Ultra-wideband (UWB) systems based on Channel State Information (CSI) estimate the position of mobile nodes within an environment by using Channel Impulse Responses(CIRs) of multiple stationary nodes. These contain spatial information caused by environment interactions such as reflections and scattering. To estimate positions from CSI of stationary nodes, we must transmit them to a centralized node. This introduces considerable communication overhead.

We present a large-scale study to determine whether CSI can be compressed into a small set of underlying latent variables that describe the most valuable information. We evaluate multiple neural network architectures containing encoding (compressing)and decoding (reconstructing) components and compare them to the state-of-the-art compression techniques Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT). We show that fully connected autoencoders achieve the lowest error, outperforming both DCT and DWT. Further experiments prove that the reconstructed CSI can be used for positioning with only mild performance deterioration at a compression of >97% and even when trained on a different environment.

Authors with CRIS profile

Thomas Robert Altstidl Machine Learning and Data Analytics Lab Sebastian Kram Lehrstuhl für Informationstechnik mit dem Schwerpunkt Kommunikationselektronik (Stiftungslehrstuhl) Oskar Herrmann Institut für Geographie Maximilian Stahlke
Tobias Feigl Lehrstuhl für Informatik 2 (Programmiersysteme) Christopher Mutschler

Related research project(s)

Recurrent Neuronal Networks (RNNs) for Real-Time Estimation of Nonlinear Motion Models (RuNN) Oct. 1, 2017 - March 31, 2021

Involved external institutions

Fraunhofer-Institut für Integrierte Schaltungen (IIS) DE Germany (DE)

How to cite

APA:

Altstidl, T.R., Kram, S., Herrmann, O., Stahlke, M., Feigl, T., & Mutschler, C. (2021). Accuracy-Aware Compression of Channel Impulse Responses using Deep Learning. In Proceedings of the International Conference on Indoor Positioning and Indoor Navigation (IPIN 2021) (pp. 1-8). Lloret de Mar, ES: IEEE Xplore.

MLA:

Altstidl, Thomas Robert, et al. "Accuracy-Aware Compression of Channel Impulse Responses using Deep Learning." Proceedings of the International Conference on Indoor Positioning and Indoor Navigation (IPIN2021), Lloret de Mar IEEE Xplore, 2021. 1-8.

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