Multipoint Channel Charting for Wireless Networks

Junquan Deng, Said Medjkouh, Nicolas Malm, Olav Tirkkonen, Christoph Studer

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

24 Citations (Scopus)


Multipoint channel charting is a machine learning framework in which multiple massive MIMO (mMIMO) base-stations (BSs) collaboratively learn a multi-cell radio map that characterizes the network environment and the users' spatial locations. The method utilizes large amounts of high-dimensional channel state information (CSI) that is passively collected from spatiotemporal samples by multiple distributed BSs. At each BS, a high-resolution multi-path channel parameter estimation algorithm extracts features hidden in the acquired CSI. Each BS then constructs a local dissimilarity matrix based on the extracted features for its collected samples and feeds it to a centralized entity which performs feature fusion and manifold learning to construct a multi-cell channel chart. The objective is to chart the radio geometry of a cellular system in such a way that the spatial distance between two users closely approximates their CSI feature distance. We demonstrate that (i) multipoint channel charting is capable of unravelling the topology of a Manhattan-grid system and (ii) the neighbor relations between CSI features from different spatial locations are captured almost perfectly.

Original languageEnglish
Title of host publicationConference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018
EditorsMichael B. Matthews
Number of pages5
ISBN (Electronic)9781538692189
Publication statusPublished - 2018
MoE publication typeA4 Article in a conference publication
EventAsilomar Conference on Signals, Systems & Computers - Pacific Grove, United States
Duration: 28 Oct 201831 Oct 2018
Conference number: 52


ConferenceAsilomar Conference on Signals, Systems & Computers
Abbreviated titleACSSC
Country/TerritoryUnited States
CityPacific Grove


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