Multipoint Channel Charting with Multiple-Input Multiple-Output Convolutional Autoencoder

Chunhua Geng, Howard Huang, and Jack Langerman

Peer Reviewed

Abstract:	We study the multipoint channel charting problem, where the channel state information (CSI) from multiple bases is used to generate channel charts for user relative positioning and many other applications. In previous work, only non-parametric methods are considered. In this paper, we fill the gap by proposing a novel neural network architecture - a multiple-input multipleoutput (MIMO) convolutional autoencoder (CAE) - to solve the problem. Based on an open-source dataset, we demonstrate that for the use cases of user relative positioning and in region location verification (IRLV), compared with a baseline autoencoder (AE) with all fully-connected layers, the proposed network is able to achieve similar or better performance with a much smaller network size. In addition, we note that the proposed network is more capable of extracting useful features from CSI data and thus more promising for end-to-end learning.
Published in:	2020 IEEE/ION Position, Location and Navigation Symposium (PLANS) April 20 - 23, 2020 Hilton Portland Downtown Portland, Oregon
Pages:	1022 - 1028
Cite this article:	Geng, Chunhua, Huang, Howard, Langerman, Jack, "Multipoint Channel Charting with Multiple-Input Multiple-Output Convolutional Autoencoder," 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS), Portland, Oregon, April 2020, pp. 1022-1028.
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