Year: 2024
Type: conference-paper
Abstract: Parametric millimeter-wave (mmWave) channel estimation involves modeling the channel matrix by combining direction-dependent signal paths, exploiting the sparse nature of mmWave channels. In our study, we propose a deep learning-based approach to estimate angle-of-arrival (AoA) and angle-of-departure (AoD) parameters from input observations in the frequency domain. To address this challenge, we have adapted a residual convolutional neural network (ResNet) to this specific problem and incorporated a technique from topological data analysis, enabling us to accurately retrieve the angular frequencies. Furthermore, we have extended this basic architecture by incorporating a posterior model fitting to enhance the system performance in terms of probability of detection. In our research, we compare the ResNet and extended ResNet approaches with state-of-the-art signal processing techniques and the Crámer-Rao lower bound through simulation. Our results indicate significant improvements in system robustness by increasing the probability of detection while maintaining a reduced estimation error.
Institutions: Universitat de València, Chalmers University of Technology
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4405975748