Iterative Learning for Reliable Underwater Link Adaptation (Student Abstract)

Authors

  • Junghun Byun Chungbuk National University
  • Yong-Ho Cho Hoseo University
  • Tae-Ho Im Hoseo University
  • Hak-Lim Ko Hoseo University
  • Kyung-Seop Shin Semyung University
  • Ohyun Jo Chungbuk National University

DOI:

https://doi.org/10.1609/aaai.v34i10.7152

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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Published

2020-04-03

How to Cite

Byun, J., Cho, Y.-H., Im, T.-H., Ko, H.-L., Shin, K.-S., & Jo, O. (2020). Iterative Learning for Reliable Underwater Link Adaptation (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 34(10), 13761-13762. https://doi.org/10.1609/aaai.v34i10.7152

Issue

Vol. 34 No. 10: Issue 10: AAAI-20 Student Tracks

Section

Student Abstract Track