Improving Channel Charting with Representation-Constrained Autoencoders

Pengzhi Huang, Oscar Castaneda, Emre Gonultas, Sa Id Medjkouh, Olav Tirkkonen, Tom Goldstein, Christoph Studer

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

5 Citations (Scopus)

Abstract

Channel charting (CC) has been proposed recently to enable logical positioning of user equipments (UEs) in the neighborhood of a multi-antenna base-station solely from channel-state information (CSI). CC relies on dimensionality reduction of high-dimensional CSI features in order to construct a channel chart that captures spatial and radio geometries so that UEs close in space are close in the channel chart. In this paper, we demonstrate that autoencoder (AE)-based CC can be augmented with side information that is obtained during the CSI acquisition process. More specifically, we propose to include pairwise representation constraints into AEs with the goal of improving the quality of the learned channel charts. We show that such representation-constrained AEs recover the global geometry of the learned channel charts, which enables CC to perform approximate positioning without global navigation satellite systems or supervised learning methods that rely on extensive and expensive measurement campaigns.

Original languageEnglish
Title of host publication2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2019
PublisherIEEE
ISBN (Electronic)978-1-5386-6528-2
ISBN (Print)978-1-5386-6529-9
DOIs
Publication statusPublished - 1 Jul 2019
MoE publication typeA4 Article in a conference publication
EventIEEE International Workshop on Signal Processing Advances in Wireless Communications - Cannes, France
Duration: 2 Jul 20195 Jul 2019
Conference number: 20

Publication series

NameIEEE International Workshop on Signal Processing Advances in Wireless Communications
ISSN (Print)2325-3789
ISSN (Electronic)1948-3252

Workshop

WorkshopIEEE International Workshop on Signal Processing Advances in Wireless Communications
Abbreviated titleSPAWC
CountryFrance
CityCannes
Period02/07/201905/07/2019

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  • Projects

    Radio Network Optimization for Heterogeneous Machine Connectivity

    Tirkkonen, O., Ponnada, S., Al-Tous, H., Pllaha, T., Kazemi, P., Vehkalahti, R. & Garau Burguera, P.

    01/01/201931/12/2021

    Project: Academy of Finland: Other research funding

    Cite this

    Huang, P., Castaneda, O., Gonultas, E., Medjkouh, S. I., Tirkkonen, O., Goldstein, T., & Studer, C. (2019). Improving Channel Charting with Representation-Constrained Autoencoders. In 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2019 [8815478] (IEEE International Workshop on Signal Processing Advances in Wireless Communications). IEEE. https://doi.org/10.1109/SPAWC.2019.8815478