Vision-Intelligence-Enabled Beam Tracking for Cross-Interface Optical Wireless Communication between Underwater and Low-Altitude Platforms
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866918508440322048 |
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| author | Liu, Jiayue Mao, Tianqi Cao, Leyu Liu, Weijie Zheng, Dezhi Cheng, Julian Wang, Zhaocheng |
| author_facet | Liu, Jiayue Mao, Tianqi Cao, Leyu Liu, Weijie Zheng, Dezhi Cheng, Julian Wang, Zhaocheng |
| contents | The rapid expansion of oceanic applications such as underwater surveillance and mineral exploration is driving the need for real-time wireless backhaul of massive observational data. Such demands are challenging to meet using the narrowband acoustic approach. Alternatively, with the participation of low-altitude platforms (LAPs), water-air optical wireless communication (OWC) has emerged as a promising solution owing to its high potential for broadband transmission. However, implementing water-air OWC remains challenging, particularly when signals penetrate the fluctuating interface, where dynamic refraction induces severe beam misalignment with airborne stations. This necessitates real-time transceiver alignment capable of adapting to complex oceanic dynamics, which remains largely unaddressed. Against this background, this paper establishes a mathematical channel model for water-air optical transmission across a time-varying sea surface. Based on the model, a vision-based beam tracking algorithm combining convolutional neural network and bi-directional long short-term memory with an attention mechanism is developed to extract key spatio-temporal features. Simulations verify that the proposed algorithm outperforms classical methods in maintaining received signal strength and suppressing vision noise, demonstrating its robustness for water-air OWC systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_21290 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Vision-Intelligence-Enabled Beam Tracking for Cross-Interface Optical Wireless Communication between Underwater and Low-Altitude Platforms Liu, Jiayue Mao, Tianqi Cao, Leyu Liu, Weijie Zheng, Dezhi Cheng, Julian Wang, Zhaocheng Signal Processing The rapid expansion of oceanic applications such as underwater surveillance and mineral exploration is driving the need for real-time wireless backhaul of massive observational data. Such demands are challenging to meet using the narrowband acoustic approach. Alternatively, with the participation of low-altitude platforms (LAPs), water-air optical wireless communication (OWC) has emerged as a promising solution owing to its high potential for broadband transmission. However, implementing water-air OWC remains challenging, particularly when signals penetrate the fluctuating interface, where dynamic refraction induces severe beam misalignment with airborne stations. This necessitates real-time transceiver alignment capable of adapting to complex oceanic dynamics, which remains largely unaddressed. Against this background, this paper establishes a mathematical channel model for water-air optical transmission across a time-varying sea surface. Based on the model, a vision-based beam tracking algorithm combining convolutional neural network and bi-directional long short-term memory with an attention mechanism is developed to extract key spatio-temporal features. Simulations verify that the proposed algorithm outperforms classical methods in maintaining received signal strength and suppressing vision noise, demonstrating its robustness for water-air OWC systems. |
| title | Vision-Intelligence-Enabled Beam Tracking for Cross-Interface Optical Wireless Communication between Underwater and Low-Altitude Platforms |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2509.21290 |