A Fast Fuzzy DRL-Based Joint Beam Design and Power Allocation for Multi-Beam GEO-LEO Coexisting Satellite Networks

Ngo, QT; He, Y; Jayawickrama, B; Dutkiewicz, E

A Fast Fuzzy DRL-Based Joint Beam Design and Power Allocation for Multi-Beam GEO-LEO Coexisting Satellite Networks

Ngo, QT He, Y

Jayawickrama, B Dutkiewicz, E

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Wireless Communications, 2025, PP, (99), pp. 1-1
Issue Date:: 2025-01-01

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Field	Value	Language
dc.contributor.author	Ngo, QT
dc.contributor.author	He, Y https://orcid.org/0000-0003-1603-9375
dc.contributor.author	Jayawickrama, B
dc.contributor.author	Dutkiewicz, E https://orcid.org/0000-0002-4268-9286
dc.date.accessioned	2025-07-31T05:18:17Z
dc.date.available	2025-07-31T05:18:17Z
dc.date.issued	2025-01-01
dc.identifier.citation	IEEE Transactions on Wireless Communications, 2025, PP, (99), pp. 1-1
dc.identifier.issn	1536-1276
dc.identifier.issn	1558-2248
dc.identifier.uri	http://hdl.handle.net/10453/188914
dc.description.abstract	As demand for ubiquitous connectivity grows, integrating satellite communications into sixth-generation (6G) networks has emerged as a crucial strategy to enhance global coverage, especially in remote and underserved regions. However, achieving the stringent performance, reliability, and spectral efficiency required for 6G presents significant challenges. Coexisting geostationary (GEO) and low Earth orbit (LEO) satellite networks offer a promising solution by enabling complementary coverage and enhanced service capabilities. Nonetheless, a critical challenge is managing intersystem interference from the LEO satellite system on the GEO system when sharing spectral resources, all while maintaining the performance of both systems. To address this, this paper introduces a fast fuzzy deep reinforcement learning (DRL)-based approach for joint beam design and power allocation in multi-beam GEO-LEO coexisting satellite networks. A robust design problem of LEO beam size and power allocation is formulated to maximize the spectral efficiency of the LEO system, considering tolerable interference on the GEO system, frequency reuse schemes employed by both GEO and LEO systems, and Doppler frequency offset induced by LEO satellite movement. A fast DRL algorithm, integrating fuzzy logic, post-decision state, and deep deterministic policy gradient, is proposed to solve this problem. Numerical results demonstrate a faster learning convergence rate for the proposed DRL algorithm compared to benchmark algorithms and confirm that the proposed method enhances LEO spectral efficiency while maintaining tolerable intersystem interference on the GEO system.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Wireless Communications
dc.relation.isbasedon	10.1109/TWC.2025.3568780
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0805 Distributed Computing, 0906 Electrical and Electronic Engineering, 1005 Communications Technologies
dc.subject.classification	Networking & Telecommunications
dc.subject.classification	4006 Communications engineering
dc.subject.classification	4008 Electrical engineering
dc.subject.classification	4606 Distributed computing and systems software
dc.title	A Fast Fuzzy DRL-Based Joint Beam Design and Power Allocation for Multi-Beam GEO-LEO Coexisting Satellite Networks
dc.type	Journal Article
utslib.citation.volume	PP
utslib.for	0805 Distributed Computing
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1005 Communications Technologies
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Global Big Data Technologies Centre (GBDTC)
utslib.copyright.status	open_access	*
dc.date.updated	2025-07-31T05:18:16Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

As demand for ubiquitous connectivity grows, integrating satellite communications into sixth-generation (6G) networks has emerged as a crucial strategy to enhance global coverage, especially in remote and underserved regions. However, achieving the stringent performance, reliability, and spectral efficiency required for 6G presents significant challenges. Coexisting geostationary (GEO) and low Earth orbit (LEO) satellite networks offer a promising solution by enabling complementary coverage and enhanced service capabilities. Nonetheless, a critical challenge is managing intersystem interference from the LEO satellite system on the GEO system when sharing spectral resources, all while maintaining the performance of both systems. To address this, this paper introduces a fast fuzzy deep reinforcement learning (DRL)-based approach for joint beam design and power allocation in multi-beam GEO-LEO coexisting satellite networks. A robust design problem of LEO beam size and power allocation is formulated to maximize the spectral efficiency of the LEO system, considering tolerable interference on the GEO system, frequency reuse schemes employed by both GEO and LEO systems, and Doppler frequency offset induced by LEO satellite movement. A fast DRL algorithm, integrating fuzzy logic, post-decision state, and deep deterministic policy gradient, is proposed to solve this problem. Numerical results demonstrate a faster learning convergence rate for the proposed DRL algorithm compared to benchmark algorithms and confirm that the proposed method enhances LEO spectral efficiency while maintaining tolerable intersystem interference on the GEO system.

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http://hdl.handle.net/10453/188914