Distributed Learning for Dynamic Channel Access in Underwater Sensor Networks
SCIE
SCOPUS
Cited 3 time in
WEB OF SCIENCE
Cited 3 time in
Scopus
-
Title
- Distributed Learning for Dynamic Channel Access in Underwater Sensor Networks
-
Author(s)
- Shin, Hui Cheol; Kim, Yongjae; Baek, Seungjae; Song, Yujae
- KIOST Author(s)
- Shin, Huicheol(신희철); Baek, Seung Jae(백승재)
-
Alternative Author(s)
- 신희철; 김용재; 백승재; 송유재
-
Publication Year
- 2020-09
-
Abstract
- In this study, the problem of dynamic channel access in distributed underwater acoustic sensor networks (UASNs) is considered. First, we formulate the dynamic channel access problem in UASNs as a multi-agent Markov decision process, wherein each underwater sensor is considered an agent whose objective is to maximize the total network throughput without coordinating with or exchanging messages among different underwater sensors. We then propose a distributed deep Q-learning-based algorithm that enables each underwater sensor to learn not only the behaviors (i.e., actions) of other sensors, but also the physical features (e.g., channel error probability) of its available acoustic channels, in order to maximize the network throughput. We conduct extensive numerical evaluations and verify that the performance of the proposed algorithm is similar to or even better than the performance of baseline algorithms, even when implemented in a distributed manner.
-
ISSN
- 1099-4300
-
URI
- https://sciwatch.kiost.ac.kr/handle/2020.kiost/35256
-
DOI
- 10.3390/e22090992
-
Bibliographic Citation
- ENTROPY, v.22, no.9, 2020
-
Publisher
- MDPI
-
Subject
- MAC PROTOCOL; INTERNET
-
Keywords
- acoustic communication; deep reinforcement learning (DRL); distributed algorithm; dynamic channel access; multi-agent RL; underwater sensor networks
-
Type
- Article
-
Language
- English
-
Document Type
- Article
- Files in This Item:
-
There are no files associated with this item.
Items in ScienceWatch@KIOST are protected by copyright, with all rights reserved, unless otherwise indicated.