Spatial Signal Design for Positioning via End-to-End Learning
Journal article, 2023

This letter considers the problem of end-to-end (E2E) learning for joint optimization of transmitter precoding and receiver processing for mmWave downlink positioning. Considering a multiple-input single-output (MISO) scenario, we propose a novel autoencoder (AE) architecture to estimate user equipment (UE) position with multiple base stations (BSs) and demonstrate that E2E learning can match model-based design, both for angle-of-departure (AoD) and position estimation, under ideal conditions without model deficits and outperform it in the presence of hardware impairments.

end-to-end learning

mmWave positioning

precoder optimization

Author

Steven Rivetti

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

José Miguel Mateos Ramos

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

Yibo Wu

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

Ericsson

Jinxiang Song

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

Musa Furkan Keskin

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

Vijaya Yajnanarayana

Ericsson

Christian Häger

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

Henk Wymeersch

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

IEEE Wireless Communications Letters

2162-2337 (ISSN) 2162-2345 (eISSN)

Vol. 12 3 525-529

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Areas of Advance

Information and Communication Technology

Subject Categories

Telecommunications

Communication Systems

Signal Processing

DOI

10.1109/LWC.2022.3233475

More information

Latest update

1/22/2024