Spatial Signal Design for Positioning Via End-to-End Learning
IEEE WIRELESS COMMUNICATIONS LETTERS(2023)
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.
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关键词
Estimation,Artificial neural networks,Signal design,MISO communication,Benchmark testing,Wireless communication,Uncertainty,mmWave positioning,precoder optimization,end-to-end learning
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