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Stacked Intelligent Metasurfaces for Wireless Communications: Applications and Challenges

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arxiv 2407.03566 v2 pith:UKZNUNUX submitted 2024-07-04 cs.IT eess.SPmath.IT

Hao Liu , Jiancheng An , Xing Jia , Lu Gan , George K. Karagiannidis , Bruno Clerckx , Mehdi Bennis , Mérouane Debbah
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Tie Jun Cui
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classification cs.ITeess.SPmath.IT
keywords wirelesscommunicationssimsapplicationschallengeshardwareintelligentmetasurfaces
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The rapid growth of wireless communications has created a significant demand for high throughput, seamless connectivity, and extremely low latency. To meet these goals, a novel technology -- stacked intelligent metasurfaces (SIMs) -- has been developed to perform signal processing by directly utilizing electromagnetic waves, thus achieving incredibly fast computing speed while reducing hardware requirements. In this article, we provide an overview of SIM technology, including its underlying hardware, benefits, and exciting applications in wireless communications. Specifically, we examine the utilization of SIMs in realizing transmit beamforming and semantic encoding in the wave domain. Additionally, channel estimation in SIM-aided communication systems is discussed. Finally, we highlight potential research opportunities and identify key challenges for deploying SIMs in wireless networks to motivate future research.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Electromagnetic Neural Network for Direction-of-Arrival Estimation

    cs.IT 2026-07 conditional novelty 5.0 of 10

    An amplitude-only, metasurface-based 'electromagnetic neural network' with a digital readout performs DOA estimation and, in simulated dual-source scenarios at high SNR, improves classification error by ~13 dB over co...

  2. Task-Oriented Low-Label Semantic Communication With Self-Supervised Learning

    cs.LG 2025-05 conditional novelty 5.0 of 10

    SLSCom pre-trains a semantic encoder with contrastive and reconstruction pretext tasks on unlabeled data, then jointly fine-tunes it with JSCC and a classifier, improving accuracy under few labels and low SNR.

  3. Unsupervised Learning-Based Joint Resource Allocation and Beamforming Design for RIS-Assisted MISO-OFDMA Systems

    eess.SP 2025-06 conditional novelty 4.0 of 10

    An unsupervised two-network system predicts RIS phases and resource block assignments, achieving near-SCA sum rate with orders of magnitude lower runtime in simulation.

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