Coding rate optimization for efficient underwater optical wireless communication
In this paper, we analyze the underwater environmental factors that determine the quality of the underwater channel and implement a Deep reinforcement learning (DRL) algorithm to adjust the coding rate using sensor information attached to the marine surface vehicle (MSV) to improve communication per...
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| Vydané v: | OCEANS 2023 - Limerick s. 1 - 4 |
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05.06.2023
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| Abstract | In this paper, we analyze the underwater environmental factors that determine the quality of the underwater channel and implement a Deep reinforcement learning (DRL) algorithm to adjust the coding rate using sensor information attached to the marine surface vehicle (MSV) to improve communication performance when establishing an underwater optical wireless communication (UOWC) link between the underwater sensor node located on the seabed and the MSV on the sea surface. The agent of the DRL model collects turbidity data in real-time and determines the number of repetitions of transmitted data to meet the required packet error rate (PER). To analyze the performance of the proposed algorithm, simulations were conducted in a virtual environment and a water tank, and both experiments achieved the required communication performance. |
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| AbstractList | In this paper, we analyze the underwater environmental factors that determine the quality of the underwater channel and implement a Deep reinforcement learning (DRL) algorithm to adjust the coding rate using sensor information attached to the marine surface vehicle (MSV) to improve communication performance when establishing an underwater optical wireless communication (UOWC) link between the underwater sensor node located on the seabed and the MSV on the sea surface. The agent of the DRL model collects turbidity data in real-time and determines the number of repetitions of transmitted data to meet the required packet error rate (PER). To analyze the performance of the proposed algorithm, simulations were conducted in a virtual environment and a water tank, and both experiments achieved the required communication performance. |
| Author | Shin, Huicheol Kim, Doyoung |
| Author_xml | – sequence: 1 givenname: Huicheol surname: Shin fullname: Shin, Huicheol email: shc0305@kiost.ac.kr organization: Korea Institute of Ocean Science & Technology,Maritime ICT & Mobility Research Department,Busan,Korea – sequence: 2 givenname: Doyoung surname: Kim fullname: Kim, Doyoung email: dykim@kiost.ac.kr organization: Korea Institute of Ocean Science & Technology,Maritime ICT & Mobility Research Department,Busan,Korea |
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| Snippet | In this paper, we analyze the underwater environmental factors that determine the quality of the underwater channel and implement a Deep reinforcement learning... |
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| SubjectTerms | Adaptive optics deep reinforcement learning Encoding modulation coding scheme Optical fiber communication Reinforcement learning Sea surface Underwater optical wireless communication Wireless communication Wireless sensor networks |
| Title | Coding rate optimization for efficient underwater optical wireless communication |
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