Deep Learning-Based Joint Beamforming Design for Multi-Hop Reconfigurable Intelligent Surface (RIS)-Aided Communication Systems
Abstract
:1. Introduction
- In the multi-hop RIS-aided multi-user communication system, the joint beamforming design, including the reflection matrices of all RISs and the transmit beamforming for all users at BS, is a challenge. In this paper, we propose a DL-based joint beamforming scheme aiming to maximize the system data rate.
- We analyze the computational complexity of the proposed DLBF method and the existing beamforming methods, which shows that the proposed method has suboptimal complexity performance. As a tradeoff, it is proved from the simulations that the data rate performance of the proposed DLBF method outperforms that of the existing method, having the optimal complexity performance.
- We investigate the effect of the RIS number on the data rate performance of multi-hop RIS-aided communication systems. The simulation results prove that the data rate performance can be significantly improved by the increasing number of RISs in the low signal-to-noise ratio (SNR) scenario, while the improvement decreases even disappears in the higher-SNR scenario. Thus, the number of RISs is suggested to be adaptively set according to the SNR value of different communication systems.
2. System Model and Problem Formulation
2.1. System Model
2.2. Problem Formulation
3. DL-Based Joint Beamforming Design
3.1. Reflection Matrix Design of RIS-1 to RIS-
3.2. DNN-Based Equivalent Beamforming Design
3.3. Decoupled Design of Equivalent Beamforming
Algorithm 1: Proposed DLBF design. |
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4. Simulation Results
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Literature | Beamforming Method | Complexity |
---|---|---|
[3] | Alternating optimization | |
[11] | Convex optimization | 1 |
[19] | Beam characteristic, ZF | |
[20] | KKT method | |
This paper | DL-based method |
Parameter | Value |
---|---|
Optimizer | Adam |
Loss function | MSE in (20) |
Epoch | 50 |
Batch size | 50 |
Training samples | 1 |
Test samples | 5 |
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Share and Cite
Chen, X.; Ye, J.; Wei, Y.; Shi, J.; Zhu, J. Deep Learning-Based Joint Beamforming Design for Multi-Hop Reconfigurable Intelligent Surface (RIS)-Aided Communication Systems. Electronics 2024, 13, 3570. https://doi.org/10.3390/electronics13173570
Chen X, Ye J, Wei Y, Shi J, Zhu J. Deep Learning-Based Joint Beamforming Design for Multi-Hop Reconfigurable Intelligent Surface (RIS)-Aided Communication Systems. Electronics. 2024; 13(17):3570. https://doi.org/10.3390/electronics13173570
Chicago/Turabian StyleChen, Xiao, Jiaoyang Ye, Yuxuan Wei, Jianfeng Shi, and Jianyue Zhu. 2024. "Deep Learning-Based Joint Beamforming Design for Multi-Hop Reconfigurable Intelligent Surface (RIS)-Aided Communication Systems" Electronics 13, no. 17: 3570. https://doi.org/10.3390/electronics13173570