Reinforcement-Learning-Enabled Beam Alignment for Water-Air Direct Optical Wireless Communications
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909306040877056 |
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| author | Liu, Jiayue Mao, Tianqi He, Dongxuan Yang, Yang Gao, Zhen Zheng, Dezhi Zhang, Jun |
| author_facet | Liu, Jiayue Mao, Tianqi He, Dongxuan Yang, Yang Gao, Zhen Zheng, Dezhi Zhang, Jun |
| contents | The escalating interests on underwater exploration/reconnaissance applications have motivated high-rate data transmission from underwater to airborne relaying platforms, especially under high-sea scenarios. Thanks to its broad bandwidth and superior confidentiality, Optical wireless communication has become one promising candidate for water-air transmission. However, the optical signals inevitably suffer from deviations when crossing the highly-dynamic water-air interfaces in the absence of relaying ships/buoys. To address the issue, this article proposes one novel beam alignment strategy based on deep reinforcement learning (DRL) for water-air direct optical wireless communications. Specifically, the dynamic water-air interface is mathematically modeled using sea-wave spectrum analysis, followed by characterization of the propagation channel with ray-tracing techniques. Then the deep deterministic policy gradient (DDPG) scheme is introduced for DRL-based transceiving beam alignment. A logarithm-exponential (LE) nonlinear reward function with respect to the received signal strength is designed for high-resolution rewarding between different actions. Simulation results validate the superiority of the proposed DRL-based beam alignment scheme. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_03250 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Reinforcement-Learning-Enabled Beam Alignment for Water-Air Direct Optical Wireless Communications Liu, Jiayue Mao, Tianqi He, Dongxuan Yang, Yang Gao, Zhen Zheng, Dezhi Zhang, Jun Signal Processing The escalating interests on underwater exploration/reconnaissance applications have motivated high-rate data transmission from underwater to airborne relaying platforms, especially under high-sea scenarios. Thanks to its broad bandwidth and superior confidentiality, Optical wireless communication has become one promising candidate for water-air transmission. However, the optical signals inevitably suffer from deviations when crossing the highly-dynamic water-air interfaces in the absence of relaying ships/buoys. To address the issue, this article proposes one novel beam alignment strategy based on deep reinforcement learning (DRL) for water-air direct optical wireless communications. Specifically, the dynamic water-air interface is mathematically modeled using sea-wave spectrum analysis, followed by characterization of the propagation channel with ray-tracing techniques. Then the deep deterministic policy gradient (DDPG) scheme is introduced for DRL-based transceiving beam alignment. A logarithm-exponential (LE) nonlinear reward function with respect to the received signal strength is designed for high-resolution rewarding between different actions. Simulation results validate the superiority of the proposed DRL-based beam alignment scheme. |
| title | Reinforcement-Learning-Enabled Beam Alignment for Water-Air Direct Optical Wireless Communications |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2409.03250 |