Dynamic Beam Scheduling of Multi-NGSO Systems Based on Deep Reinforcement Learning

Xia Xie; Yi Yang; Bowen Feng; Lirong An; Qinyu Zhang

doi:10.1109/VTC2024-Fall63153.2024.10757661

Dynamic Beam Scheduling of Multi-NGSO Systems Based on Deep Reinforcement Learning

Xia Xie^*
, Yi Yang
, Bowen Feng
, Lirong An
, Qinyu Zhang

^*Corresponding author for this work

Harbin Institute of Technology Shenzhen
Peng Cheng Laboratory

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

2 Scopus citations

Abstract

Non-geostationary orbit (NGSO) satellites, combined with beam hopping (BH) technology, play a vital role in wide-area communications, sensing, and positioning. However, the uneven distribution of ground users and the high mobility of NGSO satellites pose more significant challenges to multi-beam scheduling. This paper proposes a multi-NGSO BH scheduling framework considering the traffic demand of ground users and inter-satellite interference. We decompose the complex optimization problem into two sub-problems. First, we reallocate the satellite service cells to achieve a balanced distribution of satellite service coverage. Then, we propose a multi-NGSO BH algorithm based on soft actor-critic (SAC) to achieve real-time and traffic-driven beam scheduling. Simulation results show that the proposed algorithm outperforms other benchmarks in throughput and service failure rate (SFR).

Original language	English
Title of host publication	2024 IEEE 100th Vehicular Technology Conference, VTC 2024-Fall - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798331517786
DOIs	https://doi.org/10.1109/VTC2024-Fall63153.2024.10757661
State	Published - 2024
Externally published	Yes
Event	100th IEEE Vehicular Technology Conference, VTC 2024-Fall - Washington, United States Duration: 7 Oct 2024 → 10 Oct 2024

Publication series

Name	IEEE Vehicular Technology Conference
ISSN (Print)	1550-2252

Conference

Conference	100th IEEE Vehicular Technology Conference, VTC 2024-Fall
Country/Territory	United States
City	Washington
Period	7/10/24 → 10/10/24

Keywords

NGSO satellite communication
beam hopping
deep reinforcement learning
soft actor-critic

Access to Document

10.1109/VTC2024-Fall63153.2024.10757661

Cite this

Xie, X., Yang, Y., Feng, B., An, L., & Zhang, Q. (2024). Dynamic Beam Scheduling of Multi-NGSO Systems Based on Deep Reinforcement Learning. In 2024 IEEE 100th Vehicular Technology Conference, VTC 2024-Fall - Proceedings (IEEE Vehicular Technology Conference). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/VTC2024-Fall63153.2024.10757661

@inproceedings{923da953812040c3b6bb51e1ed134ada,

title = "Dynamic Beam Scheduling of Multi-NGSO Systems Based on Deep Reinforcement Learning",

abstract = "Non-geostationary orbit (NGSO) satellites, combined with beam hopping (BH) technology, play a vital role in wide-area communications, sensing, and positioning. However, the uneven distribution of ground users and the high mobility of NGSO satellites pose more significant challenges to multi-beam scheduling. This paper proposes a multi-NGSO BH scheduling framework considering the traffic demand of ground users and inter-satellite interference. We decompose the complex optimization problem into two sub-problems. First, we reallocate the satellite service cells to achieve a balanced distribution of satellite service coverage. Then, we propose a multi-NGSO BH algorithm based on soft actor-critic (SAC) to achieve real-time and traffic-driven beam scheduling. Simulation results show that the proposed algorithm outperforms other benchmarks in throughput and service failure rate (SFR).",

keywords = "NGSO satellite communication, beam hopping, deep reinforcement learning, soft actor-critic",

author = "Xia Xie and Yi Yang and Bowen Feng and Lirong An and Qinyu Zhang",

note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 100th IEEE Vehicular Technology Conference, VTC 2024-Fall ; Conference date: 07-10-2024 Through 10-10-2024",

year = "2024",

doi = "10.1109/VTC2024-Fall63153.2024.10757661",

language = "英语",

series = "IEEE Vehicular Technology Conference",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2024 IEEE 100th Vehicular Technology Conference, VTC 2024-Fall - Proceedings",

address = "美国",

}

Xie, X, Yang, Y, Feng, B, An, L & Zhang, Q 2024, Dynamic Beam Scheduling of Multi-NGSO Systems Based on Deep Reinforcement Learning. in 2024 IEEE 100th Vehicular Technology Conference, VTC 2024-Fall - Proceedings. IEEE Vehicular Technology Conference, Institute of Electrical and Electronics Engineers Inc., 100th IEEE Vehicular Technology Conference, VTC 2024-Fall, Washington, United States, 7/10/24. https://doi.org/10.1109/VTC2024-Fall63153.2024.10757661

Dynamic Beam Scheduling of Multi-NGSO Systems Based on Deep Reinforcement Learning. / Xie, Xia; Yang, Yi; Feng, Bowen et al.
2024 IEEE 100th Vehicular Technology Conference, VTC 2024-Fall - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2024. (IEEE Vehicular Technology Conference).

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

TY - GEN

T1 - Dynamic Beam Scheduling of Multi-NGSO Systems Based on Deep Reinforcement Learning

AU - Xie, Xia

AU - Yang, Yi

AU - Feng, Bowen

AU - An, Lirong

AU - Zhang, Qinyu

PY - 2024

Y1 - 2024

N2 - Non-geostationary orbit (NGSO) satellites, combined with beam hopping (BH) technology, play a vital role in wide-area communications, sensing, and positioning. However, the uneven distribution of ground users and the high mobility of NGSO satellites pose more significant challenges to multi-beam scheduling. This paper proposes a multi-NGSO BH scheduling framework considering the traffic demand of ground users and inter-satellite interference. We decompose the complex optimization problem into two sub-problems. First, we reallocate the satellite service cells to achieve a balanced distribution of satellite service coverage. Then, we propose a multi-NGSO BH algorithm based on soft actor-critic (SAC) to achieve real-time and traffic-driven beam scheduling. Simulation results show that the proposed algorithm outperforms other benchmarks in throughput and service failure rate (SFR).

AB - Non-geostationary orbit (NGSO) satellites, combined with beam hopping (BH) technology, play a vital role in wide-area communications, sensing, and positioning. However, the uneven distribution of ground users and the high mobility of NGSO satellites pose more significant challenges to multi-beam scheduling. This paper proposes a multi-NGSO BH scheduling framework considering the traffic demand of ground users and inter-satellite interference. We decompose the complex optimization problem into two sub-problems. First, we reallocate the satellite service cells to achieve a balanced distribution of satellite service coverage. Then, we propose a multi-NGSO BH algorithm based on soft actor-critic (SAC) to achieve real-time and traffic-driven beam scheduling. Simulation results show that the proposed algorithm outperforms other benchmarks in throughput and service failure rate (SFR).

KW - NGSO satellite communication

KW - beam hopping

KW - deep reinforcement learning

KW - soft actor-critic

UR - https://www.scopus.com/pages/publications/85213042145

U2 - 10.1109/VTC2024-Fall63153.2024.10757661

DO - 10.1109/VTC2024-Fall63153.2024.10757661

M3 - 会议稿件

AN - SCOPUS:85213042145

T3 - IEEE Vehicular Technology Conference

BT - 2024 IEEE 100th Vehicular Technology Conference, VTC 2024-Fall - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 100th IEEE Vehicular Technology Conference, VTC 2024-Fall

Y2 - 7 October 2024 through 10 October 2024

ER -

Dynamic Beam Scheduling of Multi-NGSO Systems Based on Deep Reinforcement Learning

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