MATEC Web Conf.
Volume 283, 2019The 2nd Franco-Chinese Acoustic Conference (FCAC 2018)
|Number of page(s)||6|
|Section||Ultrasounds, Signal Processing, and NDT/E|
|Published online||28 June 2019|
Reinforcement learning-based link adaptation in long delayed underwater acoustic channel
1 Beijing Normal University, CIST, 100875, 19 St. Xinjiekou Beijing, China
2 Singapore University of Technology and Design, Engineering Product Development, 487372, 8 Somapah Road, Singapore
3 Nanyang Technological University, EEE, 639798, 50 Nanyang Avenue, Singapore
* Corresponding author: email@example.com
In this paper, we apply reinforcement learning, a significant area of machine learning, to formulate an optimal self-learning strategy to interact in an unknown and dynamically variable underwater channel. The dynamic and volatile nature of the underwater channel environment makes it impossible to employ pre-knowledge. In order to select the optimal parameters to transfer data packets, by using reinforcement learning, this problem could be resolved, and better throughput could be achieved without any environmental pre-information. The slow sound velocity in an underwater scenario, means that the delay of transmitting packet acknowledgement back to sender from the receiver is material, deteriorating the convergence speed of the reinforcement learning algorithm. As reinforcement learning requires a timely acknowledgement feedback from the receiver, in this paper, we combine a juggling-like ARQ (Automatic Repeat Request) mechanism with reinforcement learning to minimize the long-delayed reward feedback problem. The simulation is accomplished by OPNET.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.