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Cited by 39Advanced Wireless Communication TechnologiesAdvanced Antenna and Metasurface TechnologiesMetamaterials and Metasurfaces Applications

Stacked Intelligent Metasurfaces for Task-Oriented Semantic Communications

Open access
Huang, Guojun, An, Jiancheng, Yang, Zhaohui, Gan, Lu, Bennis, Mehdi, Debbah, Mérouane·2025·IEEE Wireless Communications Letters
DOI 10.1109/lwc.2024.3499970
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Abstract

Semantic communication (SemCom) leveraging advanced deep learning (DL) technologies enhances the efficiency and reliability of information transmission. Emerging stacked intelligent metasurface (SIM) with an electromagnetic neural network (EMNN) architecture enables complex computations at the speed of light. In this letter, we introduce an innovative SIM-aided SemCom system for image recognition tasks, where a SIM is positioned in front of the transmitting antenna. In contrast to conventional communication systems that transmit modulated signals carrying the image information or compressed semantic information, the carrier EM wave is directly transmitted from the source. The input layer of the SIM performs source encoding, while the remaining multi-layer architecture constitutes an EMNN for semantic encoding, transforming signals into a unique beam towards a receiving antenna corresponding to the image class. Remarkably, both the source and semantic encoding occur naturally as the EM waves propagate through the SIM. At the receiver, the image is recognized by probing the received signal magnitude across the receiving array. To this end, we utilize an efficient mini-batch gradient descent algorithm to train the transmission coefficients of SIM’s meta-atoms to learn the semantic representation of the image. Extensive numerical results verify the effectiveness of utilizing the SIM-based EMNN for image recognition task-oriented SemComs, achieving more than 90% recognition accuracy.

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  • When using discrete-valued transmission coefficients with two phase shifts, nine metasurface layers are required to achieve the same concentration level as three layers with continuous-valued transmission coefficients.

    “However, when using discrete-valued transmission coefficients with two phase shifts, nine metasurface layers are required to achieve the same concentration level.”

    From Emerging Technologies in Intelligent Metasurfaces: Shaping the Future of Wireless Communications (2026) · cites this paper as [106]

    The Performance Evaluation section reports that recognition accuracy increases with the number of layers, but does not compare nine discrete-valued layers with three continuous-valued layers or measure a matching concentration level. The Conclusion mentions discrete phase shifts only as a possible optimization setting. Searches for the nine-versus-three comparison, concentration level, and two phase shifts found no supporting passage in our copy; page numbers were unavailable. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

  • Hassan et al. [106]'s simulation results showed that over 90% of the radiated power could be concentrated in the desired area using only three metasurface layers with continuous-valued transmission coefficients.

    “Simulation results showed that over 90 %of the radiated power could be concentrated in the desired area using only three metasurface layers with continuous-valued transmission coefficients.”

    From Emerging Technologies in Intelligent Metasurfaces: Shaping the Future of Wireless Communications (2026) · cites this paper as [106]

    The Performance Evaluation section reports digit-recognition accuracy, not the share of radiated power concentrated in a desired area. Although the References list a Hassan et al. paper on radiation-pattern control, our copy does not report its claimed result. Searches for the 90% figure, three layers, and radiation-pattern result found no supporting passage; page numbers were unavailable. (LLM, conf=0.78)

    Experimental — accuracy not yet measured

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Cites

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Research works printed but not linked: 23

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Cited by

Sorted by citations

Authors
Huang, G., An, J., Yang, Z., Gan, L., Bennis, M., & Debbah, M.
Affiliations
  • University of Electronic Science and Technology of China (CN)
  • Nanyang Technological University (SG)
  • Zhejiang University (CN)
  • University of Oulu (FI)
  • Center for 6G Technology, Khalifa University of Science and Technology, Abu Dhabi, UAE
ORCID
Venue
IEEE Wireless Communications Letters
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2025-02
Volume
14
Issue
2
Pages
310-314
ISSN
  • 2162-2337 (ISSN-L)
  • 2162-2345
DOI
10.1109/lwc.2024.3499970
OpenAlex
W4404479988
Cited by
39
References
3
Citations by year
2026: 15 · 2025: 23 · 2024: 1
FWCI
4.05
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Wireless Communications Letters (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • DR-NTU (Nanyang Technological University) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • Sichuan Science and Technology Program (2023YFSY0008, 2023YFG0291)
  • National Natural Science Foundation of China (62471096)
Topics
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Wireless Communication Technologies
  • Physical Sciences › Engineering › Aerospace Engineering › Advanced Antenna and Metasurface Technologies
  • Physical Sciences › Materials Science › Electronic, Optical and Magnetic Materials › Metamaterials and Metasurfaces Applications
Keywords
semantic communication, stacked intelligent metasurfaces, image recognition, meta-atoms, beam steering, source coding, semantic encoding, deep learning for wireless communications

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