Delay-aware TDMA Scheduling with Deep Reinforcement Learning in Tactical MANET

Gwangjin Wi; Sunghwa Son; Kyung Joon Park

doi:10.1109/ICTC49870.2020.9289080

Delay-aware TDMA Scheduling with Deep Reinforcement Learning in Tactical MANET

Gwangjin Wi
, Sunghwa Son
, Kyung Joon Park

Department of Electrical Engineering and Computer Science

Daegu Gyeongbuk Institute of Science and Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

14 Scopus citations

Abstract

In tactical networks, traffic should be delivered in a timely manner satisfying the quality of service (QoS) requirements for survivability and mission success. In this paper, we propose a centralized TDMA slot scheduling based on deep reinforcement learning (DRL) to guarantee the QoS requirements by minimizing end-to-end delay. We consider situations in which mission criticality of tactical traffic is dynamically changing. We introduce a DRL actor-critic algorithm to find a TDMA scheduling policy to minimize the weighted end-to-end delay which is a new metric reflecting the mission criticality of tactical traffic. The simulation results verify that the proposed scheduling policy can guarantee QoS requirements in tactical networks.

Original language	English
Title of host publication	ICTC 2020 - 11th International Conference on ICT Convergence
Subtitle of host publication	Data, Network, and AI in the Age of Untact
Publisher	IEEE Computer Society
Pages	370-372
Number of pages	3
ISBN (Electronic)	9781728167589
DOIs	https://doi.org/10.1109/ICTC49870.2020.9289080
State	Published - 21 Oct 2020
Event	11th International Conference on Information and Communication Technology Convergence, ICTC 2020 - Jeju Island, Korea, Republic of Duration: 21 Oct 2020 → 23 Oct 2020

Publication series

Name	International Conference on ICT Convergence
Volume	2020-October
ISSN (Print)	2162-1233
ISSN (Electronic)	2162-1241

Conference

Conference	11th International Conference on Information and Communication Technology Convergence, ICTC 2020
Country/Territory	Korea, Republic of
City	Jeju Island
Period	21/10/20 → 23/10/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Keywords

TDMA slot scheduling
Tactical networks
deep reinforcement learning
quality of service

Access to Document

10.1109/ICTC49870.2020.9289080

Cite this

Wi, G., Son, S., & Park, K. J. (2020). Delay-aware TDMA Scheduling with Deep Reinforcement Learning in Tactical MANET. In ICTC 2020 - 11th International Conference on ICT Convergence: Data, Network, and AI in the Age of Untact (pp. 370-372). Article 9289080 (International Conference on ICT Convergence; Vol. 2020-October). IEEE Computer Society. https://doi.org/10.1109/ICTC49870.2020.9289080

@inproceedings{b28f7f92fcca403a97b103e68186d7f8,

title = "Delay-aware TDMA Scheduling with Deep Reinforcement Learning in Tactical MANET",

abstract = "In tactical networks, traffic should be delivered in a timely manner satisfying the quality of service (QoS) requirements for survivability and mission success. In this paper, we propose a centralized TDMA slot scheduling based on deep reinforcement learning (DRL) to guarantee the QoS requirements by minimizing end-to-end delay. We consider situations in which mission criticality of tactical traffic is dynamically changing. We introduce a DRL actor-critic algorithm to find a TDMA scheduling policy to minimize the weighted end-to-end delay which is a new metric reflecting the mission criticality of tactical traffic. The simulation results verify that the proposed scheduling policy can guarantee QoS requirements in tactical networks.",

keywords = "TDMA slot scheduling, Tactical networks, deep reinforcement learning, quality of service",

author = "Gwangjin Wi and Sunghwa Son and Park, \{Kyung Joon\}",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 11th International Conference on Information and Communication Technology Convergence, ICTC 2020 ; Conference date: 21-10-2020 Through 23-10-2020",

year = "2020",

month = oct,

day = "21",

doi = "10.1109/ICTC49870.2020.9289080",

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Wi, G, Son, S & Park, KJ 2020, Delay-aware TDMA Scheduling with Deep Reinforcement Learning in Tactical MANET. in ICTC 2020 - 11th International Conference on ICT Convergence: Data, Network, and AI in the Age of Untact., 9289080, International Conference on ICT Convergence, vol. 2020-October, IEEE Computer Society, pp. 370-372, 11th International Conference on Information and Communication Technology Convergence, ICTC 2020, Jeju Island, Korea, Republic of, 21/10/20. https://doi.org/10.1109/ICTC49870.2020.9289080

Delay-aware TDMA Scheduling with Deep Reinforcement Learning in Tactical MANET. / Wi, Gwangjin; Son, Sunghwa; Park, Kyung Joon.
ICTC 2020 - 11th International Conference on ICT Convergence: Data, Network, and AI in the Age of Untact. IEEE Computer Society, 2020. p. 370-372 9289080 (International Conference on ICT Convergence; Vol. 2020-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - In tactical networks, traffic should be delivered in a timely manner satisfying the quality of service (QoS) requirements for survivability and mission success. In this paper, we propose a centralized TDMA slot scheduling based on deep reinforcement learning (DRL) to guarantee the QoS requirements by minimizing end-to-end delay. We consider situations in which mission criticality of tactical traffic is dynamically changing. We introduce a DRL actor-critic algorithm to find a TDMA scheduling policy to minimize the weighted end-to-end delay which is a new metric reflecting the mission criticality of tactical traffic. The simulation results verify that the proposed scheduling policy can guarantee QoS requirements in tactical networks.

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KW - quality of service

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Delay-aware TDMA Scheduling with Deep Reinforcement Learning in Tactical MANET

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