Abstract
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 language | English |
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Title of host publication | Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018 |
Editors | Michael B. Matthews |
Publisher | IEEE |
Pages | 286-290 |
Number of pages | 5 |
Volume | 2018-October |
ISBN (Electronic) | 9781538692189 |
DOIs | |
Publication status | Published - 2018 |
MoE publication type | A4 Conference publication |
Event | Asilomar Conference on Signals, Systems & Computers - Pacific Grove, United States Duration: 28 Oct 2018 → 31 Oct 2018 Conference number: 52 |
Conference
Conference | Asilomar Conference on Signals, Systems & Computers |
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Abbreviated title | ACSSC |
Country/Territory | United States |
City | Pacific Grove |
Period | 28/10/2018 → 31/10/2018 |