A Deep Learning-Based Low-Overhead Beam Tracking Scheme for Reconfigurable Intelligent Surface-Aided Multiple-Input and Single-Output Systems with Estimated Channels

Rongbin Guo; Guan Wang; Congyuan Xu; Rui Yin

doi:10.3390/electronics13183732

journal article Open Access Sep 20, 2024

A Deep Learning-Based Low-Overhead Beam Tracking Scheme for Reconfigurable Intelligent Surface-Aided Multiple-Input and Single-Output Systems with Estimated Channels

Rongbin Guo

Guan Wang

Congyuan Xu Rui Yin

Electronics Vol. 13 No. 18 pp. 3732 · MDPI AG

View at Publisher Save 10.3390/electronics13183732

Abstract

The next generation network requires not only an ultra-high data rate, global coverage, and connectivity, but also a reduction in 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 an RIS-aided multiple-input and single-output (MISO) system, in this paper, to obtain 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) on the user side. The simulation results verified our proposed channel estimation scheme based on PCA, and the beam tracking scheme based on a deep neural network (DNN) for semi-active RIS-aided MISO systems can obtain approximate performances to traditional hand-crafted convex optimization-based algorithms like semi-definite relaxation (SDR) with much lower computational complexity.

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Metrics

3

Citations

38

References

Details

Published: Sep 20, 2024
Vol/Issue: 13(18)
Pages: 3732
License: View

Authors

R

Rongbin Guo

School of Information and Electrical Engineering, Hangzhou City University, Hangzhou 310015, China; College of Computer Science and Technology, Zhejiang University, Hangzhou 310058, China

G

Guan Wang

School of Information and Electrical Engineering, Hangzhou City University, Hangzhou 310015, China; College of Computer Science and Technology, Zhejiang University, Hangzhou 310058, China

C

Congyuan Xu

College of Information Science and Engineering, Jiaxing University, Jiaxing 314001, China

R

Rui Yin

School of Information and Electrical Engineering, Hangzhou City University, Hangzhou 310015, China; Zhejiang Engineering Research Center of Building’s Digital Carbon Neutral Technology, Hangzhou 310015, China

Funding

National Natural Science Foundation of China Award: 2023M743063

China Postdoctoral Science Foundation Award: 2023M743063

Zhejiang Provincial Natural Science Foundation of China Award: 2023M743063

Zhejiang Provincial Postdoctoral Scholarship Award: 2023M743063

Cite This Article

Rongbin Guo, Guan Wang, Congyuan Xu, et al. (2024). A Deep Learning-Based Low-Overhead Beam Tracking Scheme for Reconfigurable Intelligent Surface-Aided Multiple-Input and Single-Output Systems with Estimated Channels. Electronics, 13(18), 3732. https://doi.org/10.3390/electronics13183732

A Deep Learning-Based Low-Overhead Beam Tracking Scheme for Reconfigurable Intelligent Surface-Aided Multiple-Input and Single-Output Systems with Estimated Channels

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