Cited 0 time in webofscience Cited 0 time in scopus

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

Title
Delay-aware TDMA Scheduling with Deep Reinforcement Learning in Tactical MANET
Authors
Wi, GwangjinSon, SunghwaPark, Kyung-Joon
DGIST Authors
Park, Kyung-Joon
Issue Date
2020-10-22
Citation
11th International Conference on Information and Communication Technology Convergence, ICTC 2020, 370-372
Type
Conference
ISBN
9781728167589
ISSN
2162-1233
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. © 2020 IEEE.
URI
http://hdl.handle.net/20.500.11750/12887
DOI
10.1109/ICTC49870.2020.9289080
Publisher
IEEE Computer Society
Related Researcher
  • Author Park, Kyung-Joon CSI(Cyber-Physical Systems Integration) Lab
  • Research Interests Cyber-Physical Systems; 무선 센서-액츄에이터 네트워크; 스마트 팩토리
Files:
There are no files associated with this item.
Collection:
Department of Information and Communication EngineeringCSI(Cyber-Physical Systems Integration) Lab2. Conference Papers


qrcode mendeley

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

BROWSE