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.
@article{arxiv.2209.12818,
title = {Spatial Signal Design for Positioning via End-to-End Learning},
author = {Steven Rivetti and Josè Miguel Mateos-Ramos and Yibo Wu and Jinxiang Song and Musa Furkan Keskin and Vijaya Yajnanarayana and Christian Häger and Henk Wymeersch},
journal= {arXiv preprint arXiv:2209.12818},
year = {2022}
}