Siamese Neural Networks for Wireless Positioning and Channel Charting

Eric Lei, Oscar Castaneda, Olav Tirkkonen, Tom Goldstein, Christoph Studer

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Abstract

Neural networks have been proposed recently for positioning and channel charting of user equipments (UEs) in wireless systems. Both of these approaches process channel state information (CSI) that is acquired at a multi-antenna basestation in order to learn a function that maps CSI to location information. CSI-based positioning using deep neural networks requires a dataset that contains both CSI and associated location information. Channel charting (CC) only requires CSI information to extract relative position information. Since CC builds on dimensionality reduction, it can be implemented using autoencoders. In this paper, we propose a unified architecture based on Siamese networks that can be used for supervised UE positioning and unsupervised channel charting. In addition, our framework enables semisupervised positioning, where only a small set of location information is available during training. We use simulations to demonstrate that Siamese networks achieve similar or better performance than existing positioning and CC approaches with a single, unified neural network architecture.

Original languageEnglish
Title of host publication2019 57th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2019
PublisherIEEE
Pages200-207
Number of pages8
ISBN (Electronic)9781728131511
DOIs
Publication statusPublished - 1 Sep 2019
MoE publication typeA4 Article in a conference publication
EventAllerton Conference on Communication, Control, and Computing - Monticello, United States
Duration: 24 Sep 201927 Sep 2019
Conference number: 57

Conference

ConferenceAllerton Conference on Communication, Control, and Computing
Abbreviated titleAllerton
CountryUnited States
CityMonticello
Period24/09/201927/09/2019

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