ML Anthology

Iterative Learning for Reliable Underwater Link Adaptation (Student Abstract)

Junghun Byun, Yong-Ho Cho, Tae-Ho Im, Hak-Lim Ko, Kyung-Seop Shin, Ohyun Jo
AAAI 2020 pp. 13761-13762
doi:10.1609/AAAI.V34I10.7152 /aaai/2020/byun2020aaai-iterative/

Abstract

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.

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Text

Byun et al. "Iterative Learning for Reliable Underwater Link Adaptation (Student Abstract)." AAAI Conference on Artificial Intelligence, 2020. doi:10.1609/AAAI.V34I10.7152

Markdown

[Byun et al. "Iterative Learning for Reliable Underwater Link Adaptation (Student Abstract)." AAAI Conference on Artificial Intelligence, 2020.](https://mlanthology.org/aaai/2020/byun2020aaai-iterative/) doi:10.1609/AAAI.V34I10.7152

BibTeX

@inproceedings{byun2020aaai-iterative,
  title     = {{Iterative Learning for Reliable Underwater Link Adaptation (Student Abstract)}},
  author    = {Byun, Junghun and Cho, Yong-Ho and Im, Tae-Ho and Ko, Hak-Lim and Shin, Kyung-Seop and Jo, Ohyun},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2020},
  pages     = {13761-13762},
  doi       = {10.1609/AAAI.V34I10.7152},
  url       = {https://mlanthology.org/aaai/2020/byun2020aaai-iterative/}
}