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Computer Science > Networking and Internet Architecture

arXiv:2212.12340 (cs)
[Submitted on 6 Dec 2022 (v1), last revised 27 Dec 2022 (this version, v2)]

Title:Channel charting based beamforming

Authors:Luc Le Magoarou (IRT b-com, Hypermedia, INSA Rennes, IETR), Taha Yassine (IRT b-com, Hypermedia, INSA Rennes, IETR), Stephane Paquelet (IRT b-com, Hypermedia, IETR), Matthieu Crussière (IRT b-com, Hypermedia, INSA Rennes, IETR)
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Abstract:Channel charting (CC) is an unsupervised learning method allowing to locate users relative to each other without reference. From a broader perspective, it can be viewed as a way to discover a low-dimensional latent space charting the channel manifold. In this paper, this latent modeling vision is leveraged together with a recently proposed location-based beamforming (LBB) method to show that channel charting can be used for mapping channels in space or frequency. Combining CC and LBB yields a neural network resembling an autoencoder. The proposed method is empirically assessed on a channel mapping task whose objective is to predict downlink channels from uplink channels.
Comments: Asilomar Conference on Signals, Systems, and Computers, Oct 2022, Pacific Grove, United States
Subjects: Networking and Internet Architecture (cs.NI); Machine Learning (cs.LG); Signal Processing (eess.SP)
Cite as: arXiv:2212.12340 [cs.NI]
  (or arXiv:2212.12340v2 [cs.NI] for this version)
  https://doi.org/10.48550/arXiv.2212.12340

Submission history

From: Luc Le Magoarou [view email] [via CCSD proxy]
[v1] Tue, 6 Dec 2022 17:40:15 UTC (955 KB)
[v2] Tue, 27 Dec 2022 15:26:32 UTC (955 KB)
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