Quantum Reinforcement Learning for Lightweight LEO Satellite Routing

Low Earth orbit (LEO) satellite networks have emerged as a promising solution, offering advantages, such as lower propagation delay, broader coverage, and rapid deployment capabilities. However, the dynamic topology and frequent handovers inherent in LEO satellite systems, coupled with limited onboa...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:IEEE internet of things journal Ročník 12; číslo 14; s. 28986 - 29004
Hlavní autori: Seon Kim, Gyu, Lee, Sungjoon, Cho, In-Sop, Park, Soohyun, Kim, Joongheon
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 15.07.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Coordinates
Deep learning
Delay time
Earth
Handover
Heuristic algorithms
Intersatellite communications
LEO satellite
Lightweight low Earth orbit (LEO) satellite routing algorithm
Low earth orbit satellites
Low earth orbits
Onboard equipment
Parameters
Planetary orbits
quantum reinforcement learning (QRL)
Rocket launches
Routing
Satellite networks
Satellites
Space vehicles
Topology
Training
ISSN:2327-4662, 2327-4662
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Abstract Low Earth orbit (LEO) satellite networks have emerged as a promising solution, offering advantages, such as lower propagation delay, broader coverage, and rapid deployment capabilities. However, the dynamic topology and frequent handovers inherent in LEO satellite systems, coupled with limited onboard computational resources, necessitate the development of efficient and lightweight routing algorithms. Therefore, this article proposes quantum reinforcement learning-based satellite routing (QRL-SR) tailored for LEO satellite networks. The QRL-SR algorithm addresses three critical considerations: 1) adapting to the dynamic and time-varying environment of LEO satellite networks; 2) incorporating LEO satellite geometry by transforming celestial coordinate data, specifically two-line element, into orbital coordinate systems for accurate LEO satellite positioning over time; and 3) being designed to be lightweight by leveraging QRL to reduce the number of training parameters. The proposed QRL-SR efficiently trains routing policies with fewer parameters, aligning with LEO satellites' small-size, weight, and power (SWaP) constraints. The primary purpose of the QRL-SR-based LEO satellites is to reduce free space path loss, delay time, and the number of hops needed for routing through the intersatellite links. Finally, experimental results demonstrate that the QRL-SR achieves routing performance comparable to or outperforms conventional algorithms while significantly reducing computational resources.
AbstractList Low Earth orbit (LEO) satellite networks have emerged as a promising solution, offering advantages, such as lower propagation delay, broader coverage, and rapid deployment capabilities. However, the dynamic topology and frequent handovers inherent in LEO satellite systems, coupled with limited onboard computational resources, necessitate the development of efficient and lightweight routing algorithms. Therefore, this article proposes quantum reinforcement learning-based satellite routing (QRL-SR) tailored for LEO satellite networks. The QRL-SR algorithm addresses three critical considerations: 1) adapting to the dynamic and time-varying environment of LEO satellite networks; 2) incorporating LEO satellite geometry by transforming celestial coordinate data, specifically two-line element, into orbital coordinate systems for accurate LEO satellite positioning over time; and 3) being designed to be lightweight by leveraging QRL to reduce the number of training parameters. The proposed QRL-SR efficiently trains routing policies with fewer parameters, aligning with LEO satellites’ small-size, weight, and power (SWaP) constraints. The primary purpose of the QRL-SR-based LEO satellites is to reduce free space path loss, delay time, and the number of hops needed for routing through the intersatellite links. Finally, experimental results demonstrate that the QRL-SR achieves routing performance comparable to or outperforms conventional algorithms while significantly reducing computational resources.
Author Kim, Joongheon
Seon Kim, Gyu
Lee, Sungjoon
Cho, In-Sop
Park, Soohyun
Author_xml – sequence: 1
  givenname: Gyu
  orcidid: 0000-0002-5559-9749
  surname: Seon Kim
  fullname: Seon Kim, Gyu
  email: kingdom0545@korea.ac.kr
  organization: Department of Electrical and Computer Engineering, Korea University, Seoul, South Korea
– sequence: 2
  givenname: Sungjoon
  orcidid: 0009-0006-0713-8223
  surname: Lee
  fullname: Lee, Sungjoon
  email: ssungjoon@korea.ac.kr
  organization: Department of Electrical and Computer Engineering, Korea University, Seoul, South Korea
– sequence: 3
  givenname: In-Sop
  orcidid: 0000-0002-1373-3479
  surname: Cho
  fullname: Cho, In-Sop
  email: lookatstar@etri.re.kr
  organization: Satellite Communication Infra Research Section, Electronics and Telecommunications Research Institute, Daejeon, South Korea
– sequence: 4
  givenname: Soohyun
  orcidid: 0000-0002-6556-9746
  surname: Park
  fullname: Park, Soohyun
  email: soohyun.park@sookmyung.ac.kr
  organization: Division of Computer Science, Sookmyung Women's University, Seoul, South Korea
– sequence: 5
  givenname: Joongheon
  orcidid: 0000-0003-2126-768X
  surname: Kim
  fullname: Kim, Joongheon
  email: joongheon@korea.ac.kr
  organization: Department of Electrical and Computer Engineering, Korea University, Seoul, South Korea
BookMark eNpNkFFLwzAQx4NMcM59AMGHgM-dSZqkzaPMqZNCcc7nkMbL7NjSmaaI396W7WEvd8fx-9_B7xqNfOMBoVtKZpQS9fC2LNczRpiYpULmXPALNGYpyxIuJRudzVdo2rZbQkgfE1TJMXp674yP3R6voPauCRb24CMuwARf-w3uV7ioN9_xF4aKi0WJP0yE3a6OgFdNF3vqBl06s2theuoT9Pm8WM9fk6J8Wc4fi8QynseEUlNx1z92StrMCZWllqhMUZVVBHLKTS6NldJB6nJWUStAMvtVWcJUlfX0BN0f7x5C89NBG_W26YLvX-qUsVwISeVA0SNlQ9O2AZw-hHpvwp-mRA--9OBLD770yVefuTtmagA445XinNH0HyCdZ4c
CODEN IITJAU
Cites_doi 10.1109/ICUFN49451.2021.9528698
10.1109/tiv.2024.3472213
10.1109/90.993300
10.1109/taes.2025.3556050
10.1109/TMC.2024.3407883
10.1109/TMC.2018.2831679
10.1109/CDC.2013.6760239
10.1145/3583780.3615240
10.23919/ACC50511.2021.9482961
10.1109/WCSP.2009.5371599
10.1109/TNSE.2024.3384452
10.1109/TVT.2021.3058126
10.1109/JIOT.2024.3468642
10.1109/ACCESS.2020.3010470
10.1038/nature01494
10.1109/TAES.2024.3415571
10.1038/s41586-021-03332-6
10.1109/TNSE.2023.3313119
10.1109/ELMAR.2006.329572
10.23919/ICACT60172.2024.10471942
10.1109/VTC2021-Fall52928.2021.9625325
10.1109/IWSSC.2007.4409398
10.1038/nature14236
10.1109/GLOBECOM46510.2021.9685686
10.1109/JIOT.2023.3234911
10.1109/TSMCB.2008.925743
10.1109/JIOT.2024.3450851
10.1109/JIOT.2024.3424782
10.1109/TMC.2023.3300314
10.1109/TNSM.2022.3186716
10.1109/JIOT.2020.3015995
10.1109/JIOT.2023.3321574
10.1109/TWC.2022.3144189
10.2307/1784698
10.1109/TNSM.2023.3274638
10.1109/JIOT.2020.3048818
10.1109/TMC.2024.3359759
10.1109/JPROC.2011.2158378
10.1109/ACCESS.2022.3194050
10.1109/TVT.2024.3470075
10.1109/JIOT.2024.3403756
10.22331/q-2022-03-30-677
10.1109/TVT.2022.3225666
10.1109/TVT.2023.3296893
10.1109/TMC.2024.3380891
10.1109/MCOM.020.2300199
10.1007/BF00992696
10.1109/TVT.2022.3217952
10.1109/JIOT.2023.3282908
10.1109/TVT.2024.3373901
10.1109/TMC.2022.3215976
10.1109/ICASSP49660.2025.10889145
10.1109/JIOT.2020.2997336
10.1109/MCOM.010.2400001
10.1109/MSN.2015.18
10.1109/PCCC.2017.8280463
ContentType Journal Article
Copyright Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2025
Copyright_xml – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2025
DBID 97E
RIA
RIE
AAYXX
CITATION
7SC
8FD
JQ2
L7M
L~C
L~D
DOI 10.1109/JIOT.2025.3568454
DatabaseName IEEE All-Society Periodicals Package (ASPP) 2005–Present
IEEE All-Society Periodicals Package (ASPP) 1998–Present
IEL
CrossRef
Computer and Information Systems Abstracts
Technology Research Database
ProQuest Computer Science Collection
Advanced Technologies Database with Aerospace
Computer and Information Systems Abstracts – Academic
Computer and Information Systems Abstracts Professional
DatabaseTitle CrossRef
Computer and Information Systems Abstracts
Technology Research Database
Computer and Information Systems Abstracts – Academic
Advanced Technologies Database with Aerospace
ProQuest Computer Science Collection
Computer and Information Systems Abstracts Professional
DatabaseTitleList Computer and Information Systems Abstracts
Database_xml – sequence: 1
  dbid: RIE
  name: IEEE Electronic Library (IEL)
  url: https://ieeexplore.ieee.org/
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
Discipline Computer Science
EISSN 2327-4662
EndPage 29004
ExternalDocumentID 10_1109_JIOT_2025_3568454
10994421
Genre orig-research
GrantInformation_xml – fundername: Ministry of Interior and Safety (MOIS)
  grantid: 2022-MOIS37-005
– fundername: Institute for Information and Communications Technology Planning and Evaluation (IITP) Grant
– fundername: Ministry of Science and ICT (MSIT) for Quantum AI Empowered Second-Life Platform Technology
  grantid: RS-2024-00439803 (SW Star Laboratory)
– fundername: Intelligent Technology Development Program on Disaster Response and Emergency Management
GroupedDBID 0R~
6IK
97E
AAJGR
AARMG
AASAJ
AAWTH
ABAZT
ABJNI
ABQJQ
ABVLG
AGQYO
AHBIQ
AKJIK
AKQYR
ALMA_UNASSIGNED_HOLDINGS
ATWAV
BEFXN
BFFAM
BGNUA
BKEBE
BPEOZ
EBS
IFIPE
IPLJI
JAVBF
M43
OCL
PQQKQ
RIA
RIE
AAYXX
CITATION
7SC
8FD
JQ2
L7M
L~C
L~D
ID FETCH-LOGICAL-c248t-11ab4f051f96c7f5973c0979197b0e814a86ac66fe3f82b1c5e62cdbc029b7973
IEDL.DBID RIE
ISICitedReferencesCount 0
ISICitedReferencesURI http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001526844600024&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN 2327-4662
IngestDate Thu Nov 20 16:50:48 EST 2025
Sat Nov 29 07:46:01 EST 2025
Wed Jul 16 07:56:28 EDT 2025
IsPeerReviewed false
IsScholarly true
Issue 14
Language English
License https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html
https://doi.org/10.15223/policy-029
https://doi.org/10.15223/policy-037
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c248t-11ab4f051f96c7f5973c0979197b0e814a86ac66fe3f82b1c5e62cdbc029b7973
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ORCID 0000-0002-1373-3479
0000-0002-5559-9749
0009-0006-0713-8223
0000-0003-2126-768X
0000-0002-6556-9746
PQID 3228556167
PQPubID 2040421
PageCount 19
ParticipantIDs ieee_primary_10994421
proquest_journals_3228556167
crossref_primary_10_1109_JIOT_2025_3568454
PublicationCentury 2000
PublicationDate 2025-07-15
PublicationDateYYYYMMDD 2025-07-15
PublicationDate_xml – month: 07
  year: 2025
  text: 2025-07-15
  day: 15
PublicationDecade 2020
PublicationPlace Piscataway
PublicationPlace_xml – name: Piscataway
PublicationTitle IEEE internet of things journal
PublicationTitleAbbrev JIoT
PublicationYear 2025
Publisher IEEE
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher_xml – name: IEEE
– name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
References ref13
ref57
ref12
ref56
ref15
(ref43) 2024
ref59
ref14
ref53
ref52
ref11
ref55
ref10
ref17
ref16
ref19
ref18
ref51
ref50
ref46
ref45
ref48
ref47
ref42
ref41
(ref44) 2024
ref49
ref8
ref7
ref9
ref4
ref3
ref6
ref5
ref40
ref35
ref34
ref37
ref36
ref31
ref30
ref33
ref32
ref2
ref1
ref39
ref38
Konda (ref54)
ref24
ref23
ref26
ref25
ref20
ref22
ref21
Mnih (ref58); 48
ref28
ref27
ref29
ref60
References_xml – ident: ref40
  doi: 10.1109/ICUFN49451.2021.9528698
– ident: ref51
  doi: 10.1109/tiv.2024.3472213
– ident: ref20
  doi: 10.1109/90.993300
– ident: ref52
  doi: 10.1109/taes.2025.3556050
– ident: ref49
  doi: 10.1109/TMC.2024.3407883
– ident: ref7
  doi: 10.1109/TMC.2018.2831679
– ident: ref50
  doi: 10.1109/CDC.2013.6760239
– ident: ref41
  doi: 10.1145/3583780.3615240
– ident: ref46
  doi: 10.23919/ACC50511.2021.9482961
– start-page: 1008
  volume-title: Proc. Adv. Neural Inf. Process. Syst. (NIPS)
  ident: ref54
  article-title: Actor-critic algorithms
– ident: ref34
  doi: 10.1109/WCSP.2009.5371599
– ident: ref25
  doi: 10.1109/TNSE.2024.3384452
– ident: ref22
  doi: 10.1109/TVT.2021.3058126
– ident: ref28
  doi: 10.1109/JIOT.2024.3468642
– ident: ref15
  doi: 10.1109/ACCESS.2020.3010470
– ident: ref42
  doi: 10.1038/nature01494
– ident: ref18
  doi: 10.1109/TAES.2024.3415571
– ident: ref57
  doi: 10.1038/s41586-021-03332-6
– ident: ref21
  doi: 10.1109/TNSE.2023.3313119
– ident: ref33
  doi: 10.1109/ELMAR.2006.329572
– ident: ref38
  doi: 10.23919/ICACT60172.2024.10471942
– volume: 48
  start-page: 1928
  volume-title: Proc. Int. Conf. Mach. Learn. (ICML)
  ident: ref58
  article-title: Asynchronous methods for deep reinforcement learning
– volume-title: Space-track.
  year: 2024
  ident: ref44
– ident: ref27
  doi: 10.1109/VTC2021-Fall52928.2021.9625325
– ident: ref35
  doi: 10.1109/IWSSC.2007.4409398
– ident: ref60
  doi: 10.1038/nature14236
– ident: ref23
  doi: 10.1109/GLOBECOM46510.2021.9685686
– ident: ref31
  doi: 10.1109/JIOT.2023.3234911
– volume-title: Celestrack.
  year: 2024
  ident: ref43
– ident: ref48
  doi: 10.1109/TSMCB.2008.925743
– ident: ref11
  doi: 10.1109/JIOT.2024.3450851
– ident: ref10
  doi: 10.1109/JIOT.2024.3424782
– ident: ref13
  doi: 10.1109/TMC.2023.3300314
– ident: ref5
  doi: 10.1109/TNSM.2022.3186716
– ident: ref3
  doi: 10.1109/JIOT.2020.3015995
– ident: ref4
  doi: 10.1109/JIOT.2023.3321574
– ident: ref17
  doi: 10.1109/TWC.2022.3144189
– ident: ref47
  doi: 10.2307/1784698
– ident: ref39
  doi: 10.1109/TNSM.2023.3274638
– ident: ref2
  doi: 10.1109/JIOT.2020.3048818
– ident: ref8
  doi: 10.1109/TMC.2024.3359759
– ident: ref37
  doi: 10.1109/JPROC.2011.2158378
– ident: ref45
  doi: 10.1109/ACCESS.2022.3194050
– ident: ref26
  doi: 10.1109/TVT.2024.3470075
– ident: ref29
  doi: 10.1109/JIOT.2024.3403756
– ident: ref55
  doi: 10.22331/q-2022-03-30-677
– ident: ref19
  doi: 10.1109/TVT.2022.3225666
– ident: ref12
  doi: 10.1109/TVT.2023.3296893
– ident: ref24
  doi: 10.1109/TMC.2024.3380891
– ident: ref16
  doi: 10.1109/MCOM.020.2300199
– ident: ref59
  doi: 10.1007/BF00992696
– ident: ref9
  doi: 10.1109/TVT.2022.3217952
– ident: ref30
  doi: 10.1109/JIOT.2023.3282908
– ident: ref14
  doi: 10.1109/TVT.2024.3373901
– ident: ref53
  doi: 10.1109/TMC.2022.3215976
– ident: ref56
  doi: 10.1109/ICASSP49660.2025.10889145
– ident: ref6
  doi: 10.1109/JIOT.2020.2997336
– ident: ref1
  doi: 10.1109/MCOM.010.2400001
– ident: ref36
  doi: 10.1109/MSN.2015.18
– ident: ref32
  doi: 10.1109/PCCC.2017.8280463
SSID ssj0001105196
Score 2.3589573
Snippet Low Earth orbit (LEO) satellite networks have emerged as a promising solution, offering advantages, such as lower propagation delay, broader coverage, and...
SourceID proquest
crossref
ieee
SourceType Aggregation Database
Index Database
Publisher
StartPage 28986
SubjectTerms Algorithms
Coordinates
Deep learning
Delay time
Earth
Handover
Heuristic algorithms
Intersatellite communications
LEO satellite
Lightweight low Earth orbit (LEO) satellite routing algorithm
Low earth orbit satellites
Low earth orbits
Onboard equipment
Parameters
Planetary orbits
quantum reinforcement learning (QRL)
Rocket launches
Routing
Satellite networks
Satellites
Space vehicles
Topology
Training
Title Quantum Reinforcement Learning for Lightweight LEO Satellite Routing
URI https://ieeexplore.ieee.org/document/10994421
https://www.proquest.com/docview/3228556167
Volume 12
WOSCitedRecordID wos001526844600024&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
journalDatabaseRights – providerCode: PRVIEE
  databaseName: IEEE Electronic Library (IEL)
  customDbUrl:
  eissn: 2327-4662
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0001105196
  issn: 2327-4662
  databaseCode: RIE
  dateStart: 20140101
  isFulltext: true
  titleUrlDefault: https://ieeexplore.ieee.org/
  providerName: IEEE
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV05T8MwFLagYmChHEUUCvLAhJQ2Tn2OCFoBqlqOgrpFjusgBlrUJvD38XNcAUIMbFH0Elmfr_e9E6HTJMkJg8XLTG4iqrWItNOUI2oY1W7k0vhcmKeBGA7lZKJuQ7K6z4Wx1vrgM9uGR-_Ln85NCaayDnhxKIW08XUheJWs9WVQIaCN8OC5dKKdm-vR2DHAhLW7jEvK6I-7xzdT-XUC-2ulX__ngLbRVtAf8Xk14Ttozc52UX3VmwGHrbqHLu9Kh1n5iu-tr41qvBkQh3Kqz9i9wgMg5h_eNooHvRF-0L48Z2ExxAk5qQZ67PfGF1dR6JgQmYTKIiJEZzR3OOSKG5E7stA1sRKKKJHFVhKqJdeG89x2c5lkxDDLEzPNTJyoTDjpfVSbzWf2AHK5TaKkUwaInFKilHb0VUPIsuN3tqtJE52tsEzfqsIYqScUsUoB-BSATwPwTdQA8L4JVrg1UWsFfxr2zjJ1R4yEpp1cHP7x2RHahL-DiZWwFqoVi9Ieow3zXrwsFyd-WXwCi4O17Q
linkProvider IEEE
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3LT8IwGG8MmuhFfGBEUXvwZDJYu25rj0YhoBN8oOG2dKUzHgQDm_779islaowHb8vyLWt-fX2_74nQKaU5CWHxhipXHpMy9qTRlD2mQibNyLmyuTBPSdzv89FI3LpkdZsLo7W2wWe6CY_Wlz-eqhJMZS3w4jAGaeOr0DrLpWt9mVQI6COR810a4dZVbzA0HJCGzSCMOAvZj9vHtlP5dQbbi6VT_eeQttCm0yDx-WLKt9GKnuyg6rI7A3abdRdd3pUGtfIV32tbHVVZQyB2BVWfsXmFE6DmH9Y6ipP2AD9IW6Cz0BgihYxUDT122sOLrud6JniKMl54hMiM5QaHXEQqzg1dCJQvYkFEnPmaEyZ5JFUU5TrIOc2ICnVE1ThTPhVZbKT3UGUyneh9yOZWVHCjDhA-ZkQIaQishKBlw_B0IEkdnS2xTN8WpTFSSyl8kQLwKQCfOuDrqAbgfRNc4FZHjSX8qds989QcMhzadkbxwR-fnaD17vAmSZNe__oQbcCfwOBKwgaqFLNSH6E19V68zGfHdol8AjR9uTY
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Quantum+Reinforcement+Learning+for+Lightweight+LEO+Satellite+Routing&rft.jtitle=IEEE+internet+of+things+journal&rft.au=Seon+Kim%2C+Gyu&rft.au=Lee%2C+Sungjoon&rft.au=Cho%2C+In-Sop&rft.au=Park%2C+Soohyun&rft.date=2025-07-15&rft.pub=IEEE&rft.eissn=2327-4662&rft.volume=12&rft.issue=14&rft.spage=28986&rft.epage=29004&rft_id=info:doi/10.1109%2FJIOT.2025.3568454&rft.externalDocID=10994421
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2327-4662&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2327-4662&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2327-4662&client=summon