Research Article
A Geometry-Gated Coordination of Reconfigurable Intelligent Surfaces for Maritime Surveillance Communications
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Abstract
Maritime surveillance communications operate across moving vessels, low-power buoys, coastal gateways, and aerial platforms while the sea surface changes the propagation geometry. This paper develops MARISCA, a geometry-gated coordination architecture for Reconfigurable Intelligent Surfaces (RISs) carried by buoys, vessels, Unmanned Aerial Vehicles (UAV), or fixed offshore infrastructure. The central design principle is that a reflected route is admissible only while both radio legs, platform pose, channel-state age, control reliability, and energy constraints are jointly valid. The paper separates three objects that are frequently conflated: The physical RIS-assisted sublink, the network controller that selects among deployment layers, and the evidence required to justify a surveillance-level claim. A narrowband model defines the direct and cascaded channels, phase quantization, imperfect channel information, and control-cycle timing. A controller specification then performs viability screening before optimization and retains a verified incumbent route when a new configuration cannot be completed inside the usable channel-state window. The literature synthesis connects maritime propagation, UAV networking, RIS channel estimation, hardware limits, and multi-RIS control. A matched-scenario evaluation protocol defines baselines, ablations, link and system metrics, energy boundaries, and failure tests. MARISCA remains a proposed architecture: No simulation or field advantage is claimed. Its contribution is a falsifiable path from maritime geometry to safe surface selection and from link improvement to mission-relevant evidence.
Keywords
Maritime communications Reconfigurable intelligent surface Offshore surveillance Unmanned aerial vehicles-assisted networks Channel-state information Geometry-aware control Reproducible evaluationReferences (38)
- [1] Alqurashi, F. S., Trichili, A., Saeed, N., Ooi, B. S., & Alouini, M. S. (2022). Maritime communications: A survey on enabling technologies, opportunities, and challenges. IEEE Internet of Things Journal, 10(4), 3525–3547. https://doi.org/10.1109/JIOT.2022.3219674
- [2] Nomikos, N., Gkonis, P. K., Bithas, P. S., & Trakadas, P. (2022). A survey on UAV-aided maritime communications: Deployment considerations, applications, and future challenges. IEEE Open Journal of the Communications Society, 4, 56–78. https://doi.org/10.1109/OJCOMS.2022.3225590
- [3] Wang, J., Zhou, H., Li, Y., Sun, Q., Wu, Y., Jin, S., & Xu, C. (2018). Wireless channel models for maritime communications. IEEE Access, 6, 68070–68088. https://doi.org/10.1109/ACCESS.2018.2879902
- [4] Habib, A., & Moh, S. (2019). Wireless channel models for over-the-sea communication: A comparative study. Applied Sciences, 9(3), 443. https://doi.org/10.3390/app9030443
- [5] Renzo, M. Di, Debbah, M., Phan Huy, D. T., Zappone, A., Alouini, M. S., & Yuen, C. (2019). Smart radio environments empowered by reconfigurable AI meta-surfaces: An idea whose time has come. EURASIP Journal on Wireless Communications and Networking, 2019(1), 1–20. https://doi.org/10.1186/s13638-019-1438-9%0A%0A
- [6] Basar, E., Di Renzo, M., De Rosny, J., Debbah, M., Alouini, M. S., & Zhang, R. (2019). Wireless communications through reconfigurable intelligent surfaces. IEEE Access, 7, 116753–116773. https://doi.org/10.1109/ACCESS.2019.2935192
- [7] Di Renzo, M., Zappone, A., Debbah, M., Alouini, M. S., Yuen, C., De Rosny, J., & Tretyakov, S. (2020). Smart radio environments empowered by reconfigurable intelligent surfaces: How it works, state of research, and the road ahead. IEEE Journal on Selected Areas in Communications, 38(11), 2450–2525. https://doi.org/10.1109/JSAC.2020.3007211
- [8] ElMossallamy, M. A., Zhang, H., Song, L., Seddik, K. G., Han, Z., & Li, G. Y. (2020). Reconfigurable intelligent surfaces for wireless communications: Principles, challenges, and opportunities. IEEE Transactions on Cognitive Communications and Networking, 6(3), 990–1002. https://doi.org/10.1109/TCCN.2020.2992604
- [9] Basharat, S., Hassan, S. A., Pervaiz, H., Mahmood, A., Ding, Z., & Gidlund, M. (2021). Reconfigurable intelligent surfaces: Potentials, applications, and challenges for 6G wireless networks. IEEE Wireless Communications, 28(6), 184–191. https://doi.org/10.1109/MWC.011.2100016
- [10] Di Renzo, M., Danufane, F. H., & Tretyakov, S. (2022). Communication models for reconfigurable intelligent surfaces: From surface electromagnetics to wireless networks optimization. Proceedings of the IEEE, 110(9), 1164–1209. https://doi.org/10.1109/JPROC.2022.3195536
- [11] Wu, Q., & Zhang, R. (2019). Intelligent reflecting surface enhanced wireless network via joint active and passive beamforming. IEEE Transactions on Wireless Communications, 18(11), 5394–5409. https://doi.org/10.1109/TWC.2019.2936025
- [12] Huang, C., Zappone, A., Alexandropoulos, G. C., Debbah, M., & Yuen, C. (2019). Reconfigurable intelligent surfaces for energy efficiency in wireless communication. IEEE Transactions on Wireless Communications, 18(8), 4157–4170. https://doi.org/10.1109/TWC.2019.2922609
- [13] Björnson, E., Özdogan, Ö., & Larsson, E. G. (2019). Intelligent reflecting surface versus decode-and-forward: How large surfaces are needed to beat relaying? IEEE Wireless Communications Letters, 9(2), 244–248. https://doi.org/10.1109/LWC.2019.2950624
- [14] Tang, W., Chen, M. Z., Chen, X., Dai, J. Y., Han, Y., Di Renzo, M., & Cui, T. J. (2020). Wireless communications with reconfigurable intelligent surface: Path loss modeling and experimental measurement. IEEE Transactions on Wireless Communications, 20(1), 421–439. https://doi.org/10.1109/TWC.2020.3024887
- [15] Björnson, E., Özdogan, Ö., & Larsson, E. G. (2021). Reconfigurable intelligent surfaces: Three myths and two critical questions. IEEE Communications Magazine, 58(12), 90–96. https://doi.org/10.1109/MCOM.001.2000407
- [16] Dajer, M., Ma, Z., Piazzi, L., Prasad, N., Qi, X. F., Sheen, B., & Yue, G. (2022). Reconfigurable intelligent surface: Design the channel--a new opportunity for future wireless networks. Digital Communications and Networks, 8(2), 87–104. https://arxiv.org/pdf/2010.07408
- [17] Zheng, B., You, C., Mei, W., & Zhang, R. (2022). A survey on channel estimation and practical passive beamforming design for intelligent reflecting surface aided wireless communications. IEEE Communications Surveys & Tutorials, 24(2), 1035–1071. https://doi.org/10.1109/COMST.2022.3155305
- [18] Yang, H., Lin, K., Xiao, L., Zhao, Y., Xiong, Z., & Han, Z. (2024). Energy harvesting UAV-RIS-assisted maritime communications based on deep reinforcement learning against jamming. IEEE Transactions on Wireless Communications, 23(8), 9854–9868. https://doi.org/10.1109/TWC.2024.3367034
- [19] Zhang, Q., Wang, L., Wu, B., & Mi, J. (2026). Reflections over the sea: Reconfigurable intelligent surface for maritime self-powered communications. ICC 2026-IEEE international conference on communications (pp. 1–6). IEEE. https://doi.org/10.1109/ICC59461.2026.11587030
- [20] Giannopoulos, A., Gkonis, P., Kalafatelis, A., Nomikos, N., Spantideas, S., Trakadas, P., & Syriopoulos, T. (2025). From 6G to SeaX-G: Integrated 6G TN/NTN for AI-assisted maritime communications—architecture, enablers, and optimization problems. Journal of Marine Science and Engineering, 13(6), 1103. https://doi.org/10.3390/jmse13061103
- [21] Nasehi, M., & Moradi, M. (2026). AI-assisted beamforming optimization in RIS-enabled 6G wireless networks under rayleigh fading channels. Soft Computing Fusion with Applications. https://doi.org/10.22105/scfa.vi.91
- [22] Li, F., Shui, Y., Liang, J., Yang, K., & Yu, J. (2023). Ship-to-ship maritime wireless channel modeling under various sea state conditions based on REL model. Frontiers in Marine Science, 10, 1134286. https://doi.org/10.3389/fmars.2023.1134286
- [23] Wang, M. M., Zhang, J., & You, X. (2020). Machine-type communication for maritime internet of things: A design. IEEE Communications Surveys & Tutorials, 22(4), 2550–2585. https://doi.org/10.1109/COMST.2020.3015694
- [24] Bergh, Ø., Danre, J. B., Stensland, K., Lima, K., Nguyen, N. T., & Heldal, R. (2024). A modular smart ocean observatory for development of sensors, underwater communication and surveillance of environmental parameters. Sensors, 24(20), 6530. https://doi.org/10.3390/s24206530
- [25] Akyildiz, I. F., Pompili, D., & Melodia, T. (2005). Underwater acoustic sensor networks: Research challenges. Ad HOC Networks, 3(3), 257–279. https://doi.org/10.1016/J.ADHOC.2005.01.004
- [26] Molina Molina, J. C., Salhaoui, M., Guerrero González, A., & Arioua, M. (2021). Autonomous marine robot based on AI recognition for permanent surveillance in marine protected areas. Sensors, 21(8), 2664. https://doi.org/10.3390/s21082664
- [27] Swindlehurst, A. L., Zhou, G., Liu, R., Pan, C., & Li, M. (2022). Channel estimation with reconfigurable intelligent surfaces—A general framework. Proceedings of the IEEE, 110(9), 1312–1338. https://doi.org/10.1109/JPROC.2022.3170358
- [28] Yu, Y., Wang, J., Zhou, X., Wang, C., Bai, Z., & Ye, Z. (2023). Review on channel estimation for reconfigurable intelligent surface assisted wireless communication system. Mathematics, 11(14), 3235. https://doi.org/10.3390/math11143235
- [29] Wang, Z., Liu, L., & Cui, S. (2020). Channel estimation for intelligent reflecting surface assisted multiuser communications. 2020 IEEE wireless communications and networking conference (WCNC) (pp. 1–6). IEEE. https://doi.org/10.1109/WCNC45663.2020.9120452
- [30] Haskou, A., & Khaleghi, H. (2023). On the effect of ris phase quantization on communications system performances. 2023 international wireless communications and mobile computing (IWCMC) (pp. 1406–1411). IEEE. https://doi.org/10.1109/IWCMC58020.2023.10182737.
- [31] Zeng, Y., Wu, Q., & Zhang, R. (2019). Accessing from the sky: A tutorial on UAV communications for 5G and beyond. Proceedings of the IEEE, 107(12), 2327–2375.
- [32] Khuwaja, A. A., Chen, Y., Zhao, N., Alouini, M. S., & Dobbins, P. (2018). A survey of channel modeling for UAV communications. IEEE Communications Surveys & Tutorials, 20(4), 2804–2821.
- [33] Lu, H., Zeng, Y., Jin, S., & Zhang, R. (2021). Aerial intelligent reflecting surface: Joint placement and passive beamforming design with 3D beam flattening. IEEE Transactions on Wireless Communications, 20(7), 4128–4143. https://doi.org/10.1109/TWC.2021.3056154
- [34] Zhou, H., Erol Kantarci, M., Liu, Y., & Poor, H. V. (2023). A survey on model-based, heuristic, and machine learning optimization approaches in RIS-aided wireless networks. IEEE communications surveys & tutorials, 26(2), 781–823. https://doi.org/10.1109/COMST.2023.3340099.
- [35] Demšar, J. (2006). Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research, 7(Jan), 1–30. https://www.jmlr.org/papers/volume7/demsar06a/demsar06a.pdf
- [36] Garcia, S., & Herrera, F. (2008). An extension on" statistical comparisons of classifiers over multiple data sets" for all pairwise comparisons. Journal of Machine Learning Research, 9(12), 2677–2694. https://www.jmlr.org/papers/volume9/garcia08a/garcia08a.pdf
- [37] Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics, 65–70. https://doi.org/10.2307/4615733
- [38] Kaur, R., Bansal, B., Majhi, S., Jain, S., Huang, C., & Yuen, C. (2024). A survey on reconfigurable intelligent surface for physical layer security of next-generation wireless communications. IEEE Open Journal of Vehicular Technology, 5, 172–199. https://doi.org/10.1109/OJVT.2023.3348658.