- Conference Paper
Rights / licenseIn Copyright - Non-Commercial Use Permitted
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. Show more
Book title2018 52nd Asilomar Conference on Signals, Systems, and Computers
Pages / Article No.
Organisational unit09695 - Studer, Christoph / Studer, Christoph
NotesConference lecture held on October 29, 2018.
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