IEICE TRANSACTIONS on Communications
Joint Channel and Power Assignment for UAV Swarm Communication Based on Multi-Agent DRL
Summary :
In this manuscript, we propose a joint channel and power assignment algorithm for an unmanned aerial vehicle (UAV) swarm communication system based on multi-agent deep reinforcement learning (DRL). Regarded as an agent, each UAV to UAV (U2U) link can choose the optimal channel and power according to the current situation after training is successfully completed. Further, a mixing network is introduced based on DRL, where Q values of every single agent are non-linearly mapped, and we call it the QMIX algorithm. As it accesses state information, QMIX can learn to enrich the joint action value function. The proposed method can be used for both unicast and multicast scenarios. Experiments show that each U2U link can be trained to meet the constraints of UAV communication and minimize the interference to the system. For unicast communication, the communication rate is increased up to 15.6% and 8.9% using the proposed DRL method compared with the well-known random and adaptive methods, respectively. For multicast communication, the communication rate is increased up to 6.7% using the proposed QMIX method compared with the DRL method and 13.6% using DRL method compared with adaptive method. Besides, the successful transmission probability can maintain a high level.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E105-B No.10 pp.1249-1257
- Publication Date
- 2022/10/01
- Publicized
- 2022/04/13
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.2021EBP3200
- Type of Manuscript
- PAPER
- Category
- Wireless Communication Technologies
Authors
Jie LI
https://orcid.org/0000-0002-7072-7817
Nanjing University of Aeronautics and Astronautics
Sai LI
Nanjing University of Aeronautics and Astronautics
Abdul Hayee SHAIKH
Nanjing University of Aeronautics and Astronautics
Keyword
Latest Issue
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Jie LI, Sai LI, Abdul Hayee SHAIKH, "Joint Channel and Power Assignment for UAV Swarm Communication Based on Multi-Agent DRL" in IEICE TRANSACTIONS on Communications,
vol. E105-B, no. 10, pp. 1249-1257, October 2022, doi: 10.1587/transcom.2021EBP3200.
Abstract: In this manuscript, we propose a joint channel and power assignment algorithm for an unmanned aerial vehicle (UAV) swarm communication system based on multi-agent deep reinforcement learning (DRL). Regarded as an agent, each UAV to UAV (U2U) link can choose the optimal channel and power according to the current situation after training is successfully completed. Further, a mixing network is introduced based on DRL, where Q values of every single agent are non-linearly mapped, and we call it the QMIX algorithm. As it accesses state information, QMIX can learn to enrich the joint action value function. The proposed method can be used for both unicast and multicast scenarios. Experiments show that each U2U link can be trained to meet the constraints of UAV communication and minimize the interference to the system. For unicast communication, the communication rate is increased up to 15.6% and 8.9% using the proposed DRL method compared with the well-known random and adaptive methods, respectively. For multicast communication, the communication rate is increased up to 6.7% using the proposed QMIX method compared with the DRL method and 13.6% using DRL method compared with adaptive method. Besides, the successful transmission probability can maintain a high level.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2021EBP3200/_p
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@ARTICLE{e105-b_10_1249,
author={Jie LI, Sai LI, Abdul Hayee SHAIKH, },
journal={IEICE TRANSACTIONS on Communications},
title={Joint Channel and Power Assignment for UAV Swarm Communication Based on Multi-Agent DRL},
year={2022},
volume={E105-B},
number={10},
pages={1249-1257},
abstract={In this manuscript, we propose a joint channel and power assignment algorithm for an unmanned aerial vehicle (UAV) swarm communication system based on multi-agent deep reinforcement learning (DRL). Regarded as an agent, each UAV to UAV (U2U) link can choose the optimal channel and power according to the current situation after training is successfully completed. Further, a mixing network is introduced based on DRL, where Q values of every single agent are non-linearly mapped, and we call it the QMIX algorithm. As it accesses state information, QMIX can learn to enrich the joint action value function. The proposed method can be used for both unicast and multicast scenarios. Experiments show that each U2U link can be trained to meet the constraints of UAV communication and minimize the interference to the system. For unicast communication, the communication rate is increased up to 15.6% and 8.9% using the proposed DRL method compared with the well-known random and adaptive methods, respectively. For multicast communication, the communication rate is increased up to 6.7% using the proposed QMIX method compared with the DRL method and 13.6% using DRL method compared with adaptive method. Besides, the successful transmission probability can maintain a high level.},
keywords={},
doi={10.1587/transcom.2021EBP3200},
ISSN={1745-1345},
month={October},}
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TY - JOUR
TI - Joint Channel and Power Assignment for UAV Swarm Communication Based on Multi-Agent DRL
T2 - IEICE TRANSACTIONS on Communications
SP - 1249
EP - 1257
AU - Jie LI
AU - Sai LI
AU - Abdul Hayee SHAIKH
PY - 2022
DO - 10.1587/transcom.2021EBP3200
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E105-B
IS - 10
JA - IEICE TRANSACTIONS on Communications
Y1 - October 2022
AB - In this manuscript, we propose a joint channel and power assignment algorithm for an unmanned aerial vehicle (UAV) swarm communication system based on multi-agent deep reinforcement learning (DRL). Regarded as an agent, each UAV to UAV (U2U) link can choose the optimal channel and power according to the current situation after training is successfully completed. Further, a mixing network is introduced based on DRL, where Q values of every single agent are non-linearly mapped, and we call it the QMIX algorithm. As it accesses state information, QMIX can learn to enrich the joint action value function. The proposed method can be used for both unicast and multicast scenarios. Experiments show that each U2U link can be trained to meet the constraints of UAV communication and minimize the interference to the system. For unicast communication, the communication rate is increased up to 15.6% and 8.9% using the proposed DRL method compared with the well-known random and adaptive methods, respectively. For multicast communication, the communication rate is increased up to 6.7% using the proposed QMIX method compared with the DRL method and 13.6% using DRL method compared with adaptive method. Besides, the successful transmission probability can maintain a high level.
ER -
English
