Version 1
: Received: 8 August 2024 / Approved: 8 August 2024 / Online: 12 August 2024 (03:24:12 CEST)
How to cite:
Guo, R.; Yuan, J.; Wang, G.; Xu, C.; Yin, R. A Deep Learning-Based Low-Overhead Beam Tracking Scheme for RIS-Aided MISO Systems with Estimated Channels. Preprints2024, 2024080679. https://doi.org/10.20944/preprints202408.0679.v1
Guo, R.; Yuan, J.; Wang, G.; Xu, C.; Yin, R. A Deep Learning-Based Low-Overhead Beam Tracking Scheme for RIS-Aided MISO Systems with Estimated Channels. Preprints 2024, 2024080679. https://doi.org/10.20944/preprints202408.0679.v1
Guo, R.; Yuan, J.; Wang, G.; Xu, C.; Yin, R. A Deep Learning-Based Low-Overhead Beam Tracking Scheme for RIS-Aided MISO Systems with Estimated Channels. Preprints2024, 2024080679. https://doi.org/10.20944/preprints202408.0679.v1
APA Style
Guo, R., Yuan, J., Wang, G., Xu, C., & Yin, R. (2024). A Deep Learning-Based Low-Overhead Beam Tracking Scheme for RIS-Aided MISO Systems with Estimated Channels. Preprints. https://doi.org/10.20944/preprints202408.0679.v1
Chicago/Turabian Style
Guo, R., Congyuan Xu and Rui Yin. 2024 "A Deep Learning-Based Low-Overhead Beam Tracking Scheme for RIS-Aided MISO Systems with Estimated Channels" Preprints. https://doi.org/10.20944/preprints202408.0679.v1
Abstract
The next generation network requires not only ultra high data rate, global coverage and connectivity, but also reducing network deployment costs and energy consumption. The emergence of reconfigurable intelligent surface (RIS) technology provides an effective way to improve efficiency and reduce cost, while the passive elements bring new challenges of channel estimation (CE) and beam tracking. For a RIS-aided multiple-input and single-output (MISO) system, in this paper, to achieve the channel state information (CSI), we propose a principle component analysis (PCA) based staged channel estimation method. Based on the estimated channel, we propose a deep learning (DL) based beam tracking scheme to realize low-complexity RIS reflection coefficient design, which effectively improves the signal-to-noise ratio (SNR) at the user side. Simulation results verify our proposed channel estimation scheme based on PCA and beam tracking scheme based on DNN for the semi-active RIS-aided MISO systems can obtain approximate performances with much lower computational complexity.
Keywords
semi-active RIS; PCA channel estimation; beam tracking; deep learning; MISO system
Subject
Computer Science and Mathematics, Signal Processing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
