An Out-of-Sample Extension for Wireless Multipoint Channel Charting
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Date
2019
Publication Type
Conference Paper
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Abstract
Channel-charting (CC) is a machine learning technique for learning a multi-cell radio map, which can be used for cognitive radio-resource-management (RRM) problems. Each base-station (BS) extracts features from the channel-state-information samples (CSI) from transmissions of user-equipment (UE) at different unknown locations. The multi-path channel components are estimated and used to construct a dissimilarity matrix between CSI samples at each BS. A fusion center combines the dissimilarity matrices of all base-stations, performs dimensional reduction based on manifold learning, constructing a Multipoint-CC (MPCC). The MPCC is a two dimension map, where the spatial difference between any pair of UEs closely approximates the distance between the clustered features. MPCC provides a mapping for any given trained UE location. To use MPCC for cognitive RRM tasks, CSI measurements for new UEs would be acquired, and these UEs would be placed on the radio map. Repeating the MPCC procedure for out-of-sample CSI measurements is computationally expensive. For this, extensions of MPCC to out-of-sample UE CSIs are investigated in this paper, when Laplacian-Eigenmaps (LE) is used for dimensional reduction. Simulation results are used to show the merits of the proposed approach.
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published
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Book title
Cognitive Radio-Oriented Wireless Networks 14th EAI International Conference, CrownCom 2019, Poznan, Poland, June 11–12, 2019, Proceedings
Volume
291
Pages / Article No.
208 - 217
Publisher
Springer
Event
14th International Conference on Cognitive Radio Oriented Wireless Networks (CrownCom 2019)
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Methods
Software
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Date created
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Organisational unit
09695 - Studer, Christoph / Studer, Christoph
Notes
Conference lecture held on June 12, 2019