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Delay-aware TDMA Scheduling with Deep Reinforcement Learning in Tactical MANET
- Delay-aware TDMA Scheduling with Deep Reinforcement Learning in Tactical MANET
- Wi, Gwangjin; Son, Sunghwa; Park, Kyung-Joon
- DGIST Authors
- Park, Kyung-Joon
- Issue Date
- 11th International Conference on Information and Communication Technology Convergence, ICTC 2020, 370-372
- 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.
- IEEE Computer Society
- Related Researcher
CSI(Cyber-Physical Systems Integration) Lab
Cyber-Physical Systems; 무선 센서-액츄에이터 네트워크; 스마트 팩토리
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- Department of Information and Communication EngineeringCSI(Cyber-Physical Systems Integration) Lab2. Conference Papers
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