Spatial Signal Design for Positioning via End-to-End Learning

Steven Rivetti; Josè Miguel Mateos-Ramos; Yibo Wu; Jinxiang Song; Musa Furkan Keskin; Vijaya Yajnanarayana; Christian Häger; Henk Wymeersch

arXiv · 2209.12818

Spatial Signal Design for Positioning via End-to-End Learning

Steven Rivetti·Josè Miguel Mateos-Ramos·Yibo Wu·Jinxiang Song·Musa Furkan Keskin·Vijaya Yajnanarayana·Christian Häger·Henk Wymeersch

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Abstract

This letter considers the problem of end-to-end 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 end-to-end 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.

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BibTeX RIS

Steven Rivetti, Josè Miguel Mateos-Ramos, Yibo Wu, Jinxiang Song, Musa Furkan Keskin, Vijaya Yajnanarayana, Christian Häger, Henk Wymeersch. 2022-09-26. Spatial Signal Design for Positioning via End-to-End Learning. https://arxiv.org/abs/2209.12818

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