The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Joint SIM Configuration and Power Allocation for Stacked Intelligent Metasurface-assisted MU-MISO Systems with TD3
Open accessPublished version of a preprint
Abstract
The stacked intelligent metasurface (SIM) emerges as an innovative technology with the ability to directly manipulate electromagnetic (EM) wave signals, drawing parallels to the operational principles of artificial neural networks (ANN). Leveraging its structure for direct EM signal processing alongside its low-power consumption, SIM holds promise for enhancing system performance within wireless communication systems. In this paper, we focus on SIM-assisted multi-user multi-input and single-output (MU-MISO) system downlink scenarios in the transmitter. We proposed a joint optimization method for SIM phase shift configuration and antenna power allocation based on the twin delayed deep deterministic policy gradient (TD3) algorithm to efficiently improve the sum rate. The results show that the proposed algorithm outperforms both deep deterministic policy gradient (DDPG) and alternating optimization (AO) algorithms. Furthermore, increasing the number of meta-atoms per layer of the SIM is always beneficial. However, continuously increasing the number of layers of SIM does not lead to sustained performance improvement.
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Jia et al. [96]'s numerical results demonstrated that the SIM can effectively mitigate interference between users while significantly reducing processing delay.
“Numerical results demonstrated that the SIM can effectively mitigate interference between users, while significantly reducing the processing delay.”
From Emerging Technologies in Intelligent Metasurfaces: Shaping the Future of Wireless Communications (2026) · cites this paper as [96]
The Problem Formulation section describes relieving multi-user interference as an optimization goal, and §D (Sum Rate versus the Number of Users) says SIM optimization alleviates the decline in sum rate as interference grows. But the paper also says transmitter-side SIM cannot completely solve multi-user interference, and its numerical results do not show a significant reduction in processing delay. The delay discussed in §E (Complexity and Convergence Analysis) is the actor-update interval, not processing delay. (LLM, conf=0.88)
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Cites
All-optical machine learning using diffractive deep neural networks
Lin, X., Rivenson, Y., Yardimci, N. T., Veli, M., Luo, Y., Jarrahi, M., & Ozcan, A. (2018) · Science · doi:10.1126/science.aat8084 · Cited by 2,568
A programmable diffractive deep neural network based on a digital-coding metasurface array
Liu, C., Ma, Q., Luo, Z. J., Hong, Q. R., Xiao, Q., Zhang, H. C., Miao, L., Yu, W. M., Cheng, Q., Li, L., et al. (2022) · Nature Electronics · doi:10.1038/s41928-022-00719-9 · Cited by 633
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Research works printed but not linked: 16
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Cited by
Sorted by citations
Emerging Technologies in Intelligent Metasurfaces: Shaping the Future of Wireless Communications
An, J., Debbah, M., Cui, T. J., Chen, Z. N., & Yuen, C. (2026) · IEEE Transactions on Antennas and Propagation · doi:10.1109/tap.2025.3571069 · Cited by 72
“Furthermore, Yang et al. [96] proposed a joint optimization method for SIM phase-shift configuration and power allocation based on the twin delayed deep deterministic policy gradient (TD3) algorithm, which achieves a higher sum rate compared with the deep deterministic policy gradient (DDPG)…”
Emerging Technologies in Intelligent Metasurfaces: Shaping the Future of Wireless Communications
An, J., Debbah, M., Cui, T. J., Chen, Z. N., & Yuen, C. (2024) · arXiv · Cited by 72
“Furthermore, Yang et al. [96] proposed a joint optimization method for SIM phase-shift configuration and power allocation based on the twin delayed deep deterministic policy gradient (TD3) algorithm, which achieves a higher sum rate compared with the deep deterministic policy gradient (DDPG)…”
Metadata
- Authors
- Yang, X., Zhang, J., Shi, E., Liu, Z., Liu, J., Zheng, K., & Ai, B.
- Affiliations
- Beijing Jiaotong University (CN)
- China Mobile (China) (CN)
- Venue
- GLOBECOM 2024 - 2024 IEEE Global Communications Conference
- Publisher
- IEEE
- Published
- 2024-12-08
- Pages
- 3255-3260
- ISSN
- 1930-529X (ISSN-L)
- OpenAlex
- W4408325124
- Cited by
- 11
- References
- 2
- Citations by year
- 2026: 6 · 2025: 5
- FWCI
- 8.43
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Globecom (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Jiangsu Province
- Fundamental Research Funds for the Central Universities
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Wireless Communication Technologies
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Antenna and Metasurface Technologies
- Physical Sciences › Materials Science › Electronic, Optical and Magnetic Materials › Metamaterials and Metasurfaces Applications
- Keywords
- stacked intelligent metasurfaces, TD3, deep reinforcement learning, sum rate maximization, meta-atoms
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