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

被引：3

作者：

Rivetti, Steven ^{[1
]}

Miguel Mateos-Ramos, Jose ^{[1
]}

Wu, Yibo ^{[1
,2
]}

Song, Jinxiang ^{[1
]}

Keskin, Musa Furkan ^{[1
]}

Yajnanarayana, Vijaya ^{[3
]}

Hager, Christian ^{[1
]}

Wymeersch, Henk ^{[1
]}

机构：

[1] Chalmers Univ Technol, Dept Elect Engn, S-41258 Gothenburg, Sweden

[2] Ericsson Res, Ericsson AB, S-41756 Stockholm, Sweden

[3] Ericsson Res, Bengaluru 560048, India

来源：

IEEE WIRELESS COMMUNICATIONS LETTERS | 2023年 / 12卷 / 03期

基金：

瑞典研究理事会;

关键词：

Estimation; Artificial neural networks; Signal design; MISO communication; Benchmark testing; Wireless communication; Uncertainty; mmWave positioning; precoder optimization; end-to-end learning; WAVE-FORM;

D O I：

10.1109/LWC.2022.3233475

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：525 / 529

页数：5

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