Deep Reinforcement Learning for Time Allocation and Directional Transmission in Joint Radar-Communication

Joash Lee; Yanyu Cheng; Dusit Niyato; Yong Liang Guan; G. David Gonzalez

doi:10.1109/WCNC51071.2022.9771580

Deep Reinforcement Learning for Time Allocation and Directional Transmission in Joint Radar-Communication

Joash Lee, Yanyu Cheng, Dusit Niyato, Yong Liang Guan, G. David Gonzalez

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

4 Citations (Scopus)

Abstract

Current strategies for joint radar-communication (JRC) rely on prior knowledge of the communication and radar systems within the vehicle network. In this paper, we propose a framework for intelligent vehicles to conduct JRC, with minimal prior knowledge, in an environment where surrounding vehicles execute radar detection periodically, which is typical in contemporary protocols. We introduce a metric on the usefulness of data to help the vehicle decide what, and to whom, data should be transmitted. The problem framework is cast as a Markov Decision Process (MDP). We show that deep reinforcement learning results in superior performance compared to nonlearning algorithms. In addition, experimental results show that the trained deep reinforcement learning agents are robust to changes in the number of vehicles in the environment.

Original language	English
Title of host publication	2022 IEEE Wireless Communications and Networking Conference, WCNC 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2559-2564
Number of pages	6
ISBN (Electronic)	9781665442664
DOIs	https://doi.org/10.1109/WCNC51071.2022.9771580
Publication status	Published - 2022
Externally published	Yes
Event	2022 IEEE Wireless Communications and Networking Conference, WCNC 2022 - Austin, United States Duration: Apr 10 2022 → Apr 13 2022

Publication series

Name	IEEE Wireless Communications and Networking Conference, WCNC
Volume	2022-April
ISSN (Print)	1525-3511

Conference

Conference	2022 IEEE Wireless Communications and Networking Conference, WCNC 2022
Country/Territory	United States
City	Austin
Period	4/10/22 → 4/13/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

ASJC Scopus Subject Areas

General Engineering

Keywords

Deep reinforcement learning
joint radar-communication
resource allocation

Access to Document

10.1109/WCNC51071.2022.9771580

Cite this

Lee, J., Cheng, Y., Niyato, D., Guan, Y. L., & David Gonzalez, G. (2022). Deep Reinforcement Learning for Time Allocation and Directional Transmission in Joint Radar-Communication. In 2022 IEEE Wireless Communications and Networking Conference, WCNC 2022 (pp. 2559-2564). (IEEE Wireless Communications and Networking Conference, WCNC; Vol. 2022-April). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/WCNC51071.2022.9771580

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title = "Deep Reinforcement Learning for Time Allocation and Directional Transmission in Joint Radar-Communication",

abstract = "Current strategies for joint radar-communication (JRC) rely on prior knowledge of the communication and radar systems within the vehicle network. In this paper, we propose a framework for intelligent vehicles to conduct JRC, with minimal prior knowledge, in an environment where surrounding vehicles execute radar detection periodically, which is typical in contemporary protocols. We introduce a metric on the usefulness of data to help the vehicle decide what, and to whom, data should be transmitted. The problem framework is cast as a Markov Decision Process (MDP). We show that deep reinforcement learning results in superior performance compared to nonlearning algorithms. In addition, experimental results show that the trained deep reinforcement learning agents are robust to changes in the number of vehicles in the environment.",

keywords = "Deep reinforcement learning, joint radar-communication, resource allocation",

author = "Joash Lee and Yanyu Cheng and Dusit Niyato and Guan, {Yong Liang} and {David Gonzalez}, G.",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE Wireless Communications and Networking Conference, WCNC 2022 ; Conference date: 10-04-2022 Through 13-04-2022",

year = "2022",

doi = "10.1109/WCNC51071.2022.9771580",

language = "English",

series = "IEEE Wireless Communications and Networking Conference, WCNC",

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Lee, J, Cheng, Y, Niyato, D, Guan, YL & David Gonzalez, G 2022, Deep Reinforcement Learning for Time Allocation and Directional Transmission in Joint Radar-Communication. in 2022 IEEE Wireless Communications and Networking Conference, WCNC 2022. IEEE Wireless Communications and Networking Conference, WCNC, vol. 2022-April, Institute of Electrical and Electronics Engineers Inc., pp. 2559-2564, 2022 IEEE Wireless Communications and Networking Conference, WCNC 2022, Austin, United States, 4/10/22. https://doi.org/10.1109/WCNC51071.2022.9771580

Deep Reinforcement Learning for Time Allocation and Directional Transmission in Joint Radar-Communication. / Lee, Joash; Cheng, Yanyu; Niyato, Dusit et al.
2022 IEEE Wireless Communications and Networking Conference, WCNC 2022. Institute of Electrical and Electronics Engineers Inc., 2022. p. 2559-2564 (IEEE Wireless Communications and Networking Conference, WCNC; Vol. 2022-April).

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

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AU - Guan, Yong Liang

AU - David Gonzalez, G.

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N2 - Current strategies for joint radar-communication (JRC) rely on prior knowledge of the communication and radar systems within the vehicle network. In this paper, we propose a framework for intelligent vehicles to conduct JRC, with minimal prior knowledge, in an environment where surrounding vehicles execute radar detection periodically, which is typical in contemporary protocols. We introduce a metric on the usefulness of data to help the vehicle decide what, and to whom, data should be transmitted. The problem framework is cast as a Markov Decision Process (MDP). We show that deep reinforcement learning results in superior performance compared to nonlearning algorithms. In addition, experimental results show that the trained deep reinforcement learning agents are robust to changes in the number of vehicles in the environment.

AB - Current strategies for joint radar-communication (JRC) rely on prior knowledge of the communication and radar systems within the vehicle network. In this paper, we propose a framework for intelligent vehicles to conduct JRC, with minimal prior knowledge, in an environment where surrounding vehicles execute radar detection periodically, which is typical in contemporary protocols. We introduce a metric on the usefulness of data to help the vehicle decide what, and to whom, data should be transmitted. The problem framework is cast as a Markov Decision Process (MDP). We show that deep reinforcement learning results in superior performance compared to nonlearning algorithms. In addition, experimental results show that the trained deep reinforcement learning agents are robust to changes in the number of vehicles in the environment.

KW - Deep reinforcement learning

KW - joint radar-communication

KW - resource allocation

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Lee J, Cheng Y, Niyato D, Guan YL, David Gonzalez G. Deep Reinforcement Learning for Time Allocation and Directional Transmission in Joint Radar-Communication. In 2022 IEEE Wireless Communications and Networking Conference, WCNC 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 2559-2564. (IEEE Wireless Communications and Networking Conference, WCNC). doi: 10.1109/WCNC51071.2022.9771580

Deep Reinforcement Learning for Time Allocation and Directional Transmission in Joint Radar-Communication

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus Subject Areas

Keywords

Access to Document

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Cite this