Journal article
User capacity analysis of intelligent omni-surface assisted indoor mmWave networks
- Abstract:
- The intelligent omni-surface (IOS), evolved from the reconfigurable intelligent surface (RIS), is viewed as a promising technology for next-generation wireless communications thanks to its more flexible deployment. Unlike conventional RISs that can only operate on one side to receive and reflect signals, an IOS can simultaneously reflect and refract incident waves, thereby achieving full-space coverage. In this paper, tools from stochastic geometry are applied to model and analyze the performance of IOS-assisted millimeter-wave (mmWave) communications, with a particular focus on indoor scenarios. The dimensions of the indoor environment and the IOS aperture are explicitly taken into account to evaluate how many end users an IOS can serve, i.e., its user capacity. To this end, we characterize the users’ coverage performance under practical propagation and service constraints, where near-field effects and heterogeneous user-type signal-to-interference-plus-noise-ratio (SINR) thresholds are incorporated to enable accurate capacity evaluation. Both near-field and far-field regions are considered to capture realistic indoor operation. Furthermore, a comprehensive set of system parameters, including node heights and the depth-offocus (DF) effect, is incorporated into the mathematical analysis. We also compare the user capacity of IOS-assisted and RISassisted indoor mmWave systems under hybrid near-/far-field and pure far-field conditions. The analytical results are validated through Monte Carlo simulations, confirming the accuracy of the proposed framework. The obtained insights show that near-field effects can substantially affect user capacity at certain IOS/RIS aperture sizes, highlighting the importance of accounting for such effects for accurate modeling and design of IOS-assisted indoor networks.
- Publication status:
- Accepted
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Accepted manuscript, pdf, 1.6MB, Terms of use)
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- Publisher copy:
- 10.1109/tvt.2026.3691669
Authors
- Publisher:
- Institute of Electrical and Electronics Engineers
- Journal:
- IEEE Transactions on Vehicular Technology More from this journal
- Publication date:
- 2026-05-08
- Acceptance date:
- 2026-04-29
- DOI:
- EISSN:
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1939-9359
- ISSN:
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0018-9545
- Language:
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English
- Pubs id:
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2413205
- Local pid:
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pubs:2413205
- Deposit date:
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2026-05-01
- ARK identifier:
Terms of use
- Copyright holder:
- IEEE
- Copyright date:
- 2026
- Rights statement:
- © 2026 IEEE. All rights reserved, including rights for text and data mining and training of artificial intelligence and similar technologies. Personal use is permitted, but republication/redistribution requires IEEE permission.
- Notes:
- The author accepted manuscript (AAM) of this paper has been made available under the University of Oxford's Open Access Publications Policy, and a CC BY public copyright licence has been applied.
- Licence:
- CC Attribution (CC BY)
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