Deep Reinforcement Learning Approach for Fairness-aware Scheduling in Wireless Networks

Minseok Kim; Sangwon Hwang; Inkyu Lee

doi:10.1109/ICTC55196.2022.9952897

Deep Reinforcement Learning Approach for Fairness-aware Scheduling in Wireless Networks

Minseok Kim, Sangwon Hwang, Inkyu Lee

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

2 Citations (Scopus)

Abstract

This paper studies a scheduling problem in wireless network systems which have combinatorial and time-varying properties. Although studies on efficient scheduling algorithms have been widely investigated, a huge trade-off between the performance and complexity still exists. Furthermore, it is becoming necessary to consider quality of service (QoS) and fairness constraints as growing demands on wireless networks. To this end, this paper propose a deep reinforcement learning (DRL) approach which can maximize system throughput while considering QoS and fairness by designing DRL structure according to the objective function and constraints. Numerical results demonstrate the effectiveness of the proposed approach.

Original language	English
Title of host publication	ICTC 2022 - 13th International Conference on Information and Communication Technology Convergence
Subtitle of host publication	Accelerating Digital Transformation with ICT Innovation
Publisher	IEEE Computer Society
Pages	1229-1232
Number of pages	4
ISBN (Electronic)	9781665499392
DOIs	https://doi.org/10.1109/ICTC55196.2022.9952897
Publication status	Published - 2022
Event	13th International Conference on Information and Communication Technology Convergence, ICTC 2022 - Jeju Island, Korea, Republic of Duration: 2022 Oct 19 → 2022 Oct 21

Publication series

Name	International Conference on ICT Convergence
Volume	2022-October
ISSN (Print)	2162-1233
ISSN (Electronic)	2162-1241

Conference

Conference	13th International Conference on Information and Communication Technology Convergence, ICTC 2022
Country/Territory	Korea, Republic of
City	Jeju Island
Period	22/10/19 → 22/10/21

Bibliographical note

Funding Information:
ACKNOWLEDGMENT This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government (MSIT) (No. 2022R1A5A1027646).

Publisher Copyright:
© 2022 IEEE.

Keywords

Deep reinforcement learning
fairness-aware
scheduling algorithm

ASJC Scopus subject areas

Information Systems
Computer Networks and Communications

Access to Document

10.1109/ICTC55196.2022.9952897

Cite this

Kim, M., Hwang, S., & Lee, I. (2022). Deep Reinforcement Learning Approach for Fairness-aware Scheduling in Wireless Networks. In ICTC 2022 - 13th International Conference on Information and Communication Technology Convergence: Accelerating Digital Transformation with ICT Innovation (pp. 1229-1232). (International Conference on ICT Convergence; Vol. 2022-October). IEEE Computer Society. https://doi.org/10.1109/ICTC55196.2022.9952897

Deep Reinforcement Learning Approach for Fairness-aware Scheduling in Wireless Networks. / Kim, Minseok; Hwang, Sangwon; Lee, Inkyu.
ICTC 2022 - 13th International Conference on Information and Communication Technology Convergence: Accelerating Digital Transformation with ICT Innovation. IEEE Computer Society, 2022. p. 1229-1232 (International Conference on ICT Convergence; Vol. 2022-October).

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

Kim, M, Hwang, S & Lee, I 2022, Deep Reinforcement Learning Approach for Fairness-aware Scheduling in Wireless Networks. in ICTC 2022 - 13th International Conference on Information and Communication Technology Convergence: Accelerating Digital Transformation with ICT Innovation. International Conference on ICT Convergence, vol. 2022-October, IEEE Computer Society, pp. 1229-1232, 13th International Conference on Information and Communication Technology Convergence, ICTC 2022, Jeju Island, Korea, Republic of, 22/10/19. https://doi.org/10.1109/ICTC55196.2022.9952897

Kim M, Hwang S, Lee I. Deep Reinforcement Learning Approach for Fairness-aware Scheduling in Wireless Networks. In ICTC 2022 - 13th International Conference on Information and Communication Technology Convergence: Accelerating Digital Transformation with ICT Innovation. IEEE Computer Society. 2022. p. 1229-1232. (International Conference on ICT Convergence). doi: 10.1109/ICTC55196.2022.9952897

Kim, Minseok ; Hwang, Sangwon ; Lee, Inkyu. / Deep Reinforcement Learning Approach for Fairness-aware Scheduling in Wireless Networks. ICTC 2022 - 13th International Conference on Information and Communication Technology Convergence: Accelerating Digital Transformation with ICT Innovation. IEEE Computer Society, 2022. pp. 1229-1232 (International Conference on ICT Convergence).

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title = "Deep Reinforcement Learning Approach for Fairness-aware Scheduling in Wireless Networks",

abstract = "This paper studies a scheduling problem in wireless network systems which have combinatorial and time-varying properties. Although studies on efficient scheduling algorithms have been widely investigated, a huge trade-off between the performance and complexity still exists. Furthermore, it is becoming necessary to consider quality of service (QoS) and fairness constraints as growing demands on wireless networks. To this end, this paper propose a deep reinforcement learning (DRL) approach which can maximize system throughput while considering QoS and fairness by designing DRL structure according to the objective function and constraints. Numerical results demonstrate the effectiveness of the proposed approach.",

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N2 - This paper studies a scheduling problem in wireless network systems which have combinatorial and time-varying properties. Although studies on efficient scheduling algorithms have been widely investigated, a huge trade-off between the performance and complexity still exists. Furthermore, it is becoming necessary to consider quality of service (QoS) and fairness constraints as growing demands on wireless networks. To this end, this paper propose a deep reinforcement learning (DRL) approach which can maximize system throughput while considering QoS and fairness by designing DRL structure according to the objective function and constraints. Numerical results demonstrate the effectiveness of the proposed approach.

AB - This paper studies a scheduling problem in wireless network systems which have combinatorial and time-varying properties. Although studies on efficient scheduling algorithms have been widely investigated, a huge trade-off between the performance and complexity still exists. Furthermore, it is becoming necessary to consider quality of service (QoS) and fairness constraints as growing demands on wireless networks. To this end, this paper propose a deep reinforcement learning (DRL) approach which can maximize system throughput while considering QoS and fairness by designing DRL structure according to the objective function and constraints. Numerical results demonstrate the effectiveness of the proposed approach.

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Deep Reinforcement Learning Approach for Fairness-aware Scheduling in Wireless Networks

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

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