Optimal Power and Position Control for UAV-assisted JCR Networks: Multi-Agent Q-Learning Approach

Ji Min Park; Howon Lee; Heejung Yu

doi:10.1109/CCNC51644.2023.10060439

Optimal Power and Position Control for UAV-assisted JCR Networks: Multi-Agent Q-Learning Approach

Ji Min Park
, Howon Lee
, Heejung Yu

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

1 Citation (Scopus)

Abstract

In wireless communication networks including unmanned aerial vehicles (UAVs), joint communication and radar (JCR) using a single waveform for both communication and sensing functions has been considered. In the JCR, the power allocation to the pilot and data parts can be optimized in terms of communication and sensing performance metrics. Furthermore, to serve ground users effectively, the location of UAVs, which receive the transmit signal from a base-station (BS) and forward to ground users, should be optimized. In multi-UAV environments, the optimization of signal power and UAV's position becomes too complicated to solve with a conventional optimization framework. Therefore, a reinforcement learning approach, i.e., multi-agent Q-learning, is adopted to optimize the UAV-assisted JCR networks.

Original language	English
Title of host publication	2023 IEEE 20th Consumer Communications and Networking Conference, CCNC 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	700-701
Number of pages	2
ISBN (Electronic)	9781665497343
DOIs	https://doi.org/10.1109/CCNC51644.2023.10060439
Publication status	Published - 2023
Event	20th IEEE Consumer Communications and Networking Conference, CCNC 2023 - Las Vegas, United States Duration: 2023 Jan 8 → 2023 Jan 11

Publication series

Name	Proceedings - IEEE Consumer Communications and Networking Conference, CCNC
Volume	2023-January
ISSN (Print)	2331-9860

Conference

Conference	20th IEEE Consumer Communications and Networking Conference, CCNC 2023
Country/Territory	United States
City	Las Vegas
Period	23/1/8 → 23/1/11

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

JCR
multi-agent Q-learning
UAV-assisted network

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Computer Vision and Pattern Recognition
Electrical and Electronic Engineering

Access to Document

10.1109/CCNC51644.2023.10060439

Cite this

Park, J. M., Lee, H., & Yu, H. (2023). Optimal Power and Position Control for UAV-assisted JCR Networks: Multi-Agent Q-Learning Approach. In 2023 IEEE 20th Consumer Communications and Networking Conference, CCNC 2023 (pp. 700-701). (Proceedings - IEEE Consumer Communications and Networking Conference, CCNC; Vol. 2023-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CCNC51644.2023.10060439

Optimal Power and Position Control for UAV-assisted JCR Networks: Multi-Agent Q-Learning Approach. / Park, Ji Min; Lee, Howon; Yu, Heejung.
2023 IEEE 20th Consumer Communications and Networking Conference, CCNC 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 700-701 (Proceedings - IEEE Consumer Communications and Networking Conference, CCNC; Vol. 2023-January).

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

Park, JM, Lee, H & Yu, H 2023, Optimal Power and Position Control for UAV-assisted JCR Networks: Multi-Agent Q-Learning Approach. in 2023 IEEE 20th Consumer Communications and Networking Conference, CCNC 2023. Proceedings - IEEE Consumer Communications and Networking Conference, CCNC, vol. 2023-January, Institute of Electrical and Electronics Engineers Inc., pp. 700-701, 20th IEEE Consumer Communications and Networking Conference, CCNC 2023, Las Vegas, United States, 23/1/8. https://doi.org/10.1109/CCNC51644.2023.10060439

Park JM, Lee H, Yu H. Optimal Power and Position Control for UAV-assisted JCR Networks: Multi-Agent Q-Learning Approach. In 2023 IEEE 20th Consumer Communications and Networking Conference, CCNC 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 700-701. (Proceedings - IEEE Consumer Communications and Networking Conference, CCNC). doi: 10.1109/CCNC51644.2023.10060439

Park, Ji Min ; Lee, Howon ; Yu, Heejung. / Optimal Power and Position Control for UAV-assisted JCR Networks : Multi-Agent Q-Learning Approach. 2023 IEEE 20th Consumer Communications and Networking Conference, CCNC 2023. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 700-701 (Proceedings - IEEE Consumer Communications and Networking Conference, CCNC).

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title = "Optimal Power and Position Control for UAV-assisted JCR Networks: Multi-Agent Q-Learning Approach",

abstract = "In wireless communication networks including unmanned aerial vehicles (UAVs), joint communication and radar (JCR) using a single waveform for both communication and sensing functions has been considered. In the JCR, the power allocation to the pilot and data parts can be optimized in terms of communication and sensing performance metrics. Furthermore, to serve ground users effectively, the location of UAVs, which receive the transmit signal from a base-station (BS) and forward to ground users, should be optimized. In multi-UAV environments, the optimization of signal power and UAV's position becomes too complicated to solve with a conventional optimization framework. Therefore, a reinforcement learning approach, i.e., multi-agent Q-learning, is adopted to optimize the UAV-assisted JCR networks.",

keywords = "JCR, multi-agent Q-learning, UAV-assisted network",

author = "Park, \{Ji Min\} and Howon Lee and Heejung Yu",

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AB - In wireless communication networks including unmanned aerial vehicles (UAVs), joint communication and radar (JCR) using a single waveform for both communication and sensing functions has been considered. In the JCR, the power allocation to the pilot and data parts can be optimized in terms of communication and sensing performance metrics. Furthermore, to serve ground users effectively, the location of UAVs, which receive the transmit signal from a base-station (BS) and forward to ground users, should be optimized. In multi-UAV environments, the optimization of signal power and UAV's position becomes too complicated to solve with a conventional optimization framework. Therefore, a reinforcement learning approach, i.e., multi-agent Q-learning, is adopted to optimize the UAV-assisted JCR networks.

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Optimal Power and Position Control for UAV-assisted JCR Networks: Multi-Agent Q-Learning Approach

Abstract

Publication series

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Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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