Siamese Neural Networks for Wireless Positioning and Channel Charting

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussavertaisarvioitu

Tutkijat

Organisaatiot

  • Cornell University
  • University of Maryland, College Park

Kuvaus

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.

Yksityiskohdat

AlkuperäiskieliEnglanti
Otsikko2019 57th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2019
TilaJulkaistu - 1 syyskuuta 2019
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaAllerton Conference on Communication, Control, and Computing - Monticello, Yhdysvallat
Kesto: 24 syyskuuta 201927 syyskuuta 2019
Konferenssinumero: 57

Conference

ConferenceAllerton Conference on Communication, Control, and Computing
LyhennettäAllerton
MaaYhdysvallat
KaupunkiMonticello
Ajanjakso24/09/201927/09/2019

ID: 40452874