Iterative Learning for Reliable Underwater Link Adaptation (Student Abstract)
| العنوان: | Iterative Learning for Reliable Underwater Link Adaptation (Student Abstract) |
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| المؤلفون: | JungHun Byun, Tae-Ho Im, Ohyun Jo, Hak-Lim Ko, Kyung-Seop Shin, Yong-Ho Cho |
| المصدر: | AAAI |
| بيانات النشر: | Association for the Advancement of Artificial Intelligence (AAAI), 2020. |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | Computer engineering, Orthogonal frequency-division multiplexing, Computer science, Iterative learning control, Benchmark (computing), Link adaptation, General Medicine, Underwater |
| الوصف: | This paper describes an iterative learning framework consisting of multi-layer prediction processes for underwater link adaptation. To obtain a dataset in real underwater environments, we implemented OFDM (Orthogonal Frequency Division Multiplexing)-based acoustic communications testbeds for the first time. Actual underwater data measured in Yellow Sea, South Korea, were used for training the iterative learning model. Remarkably, the iterative learning model achieves up to 25% performance improvement over the conventional benchmark model. |
| تدمد: | 2374-3468 2159-5399 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_________::3855968934d14ca2ca3e3d9cc38a14a5Test https://doi.org/10.1609/aaai.v34i10.7152Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi...........3855968934d14ca2ca3e3d9cc38a14a5 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 23743468 21595399 |
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