Deep Reinforcement Learning-Based Multipath Routing for LEO Megaconstellation Networks
The expansion of megaconstellation networks (MCNs) represents a promising solution for achieving global Internet coverage. To meet the growing demand for satellite services, multipath routing allows the simultaneous establishment of multiple transmission paths, enabling the transmission of flows in...
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| Veröffentlicht in: | Electronics (Basel) Jg. 13; H. 15; S. 3054 |
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| Abstract | The expansion of megaconstellation networks (MCNs) represents a promising solution for achieving global Internet coverage. To meet the growing demand for satellite services, multipath routing allows the simultaneous establishment of multiple transmission paths, enabling the transmission of flows in parallel. Nevertheless, the mobility of satellites and time-varying link states presents a challenge for the discovery of optimal paths and traffic scheduling in multipath routing. Given the inflexibility of traditional static deep reinforcement learning (DRL)-based routing algorithms in dealing with time-varying constellation topologies, DRL-based multipath routing (DMR) enabled by a graph neural network (GNN) is proposed as a means of enhancing the transmission performance of MCNs. DMR decouples the stochastic optimization problem of multipath routing under traffic and bandwidth constraints into two subproblems: multipath routing discovery and multipath traffic scheduling. Firstly, the minimum hop count-based multipath route discovery algorithm (MHMRD) is proposed for the computation of multiple available paths between all source and destination nodes. Secondly, the GNN-based multipath traffic scheduling scheme (GMTS) is proposed as a means of dynamically scheduling the traffic on each available path for each data stream, based on the state information of ISLs and traffic demand. Simulation results demonstrate that the proposed scheme can be scaled to constellations with different configurations without the necessity for repeated training and enhance the throughput, completion ratio, and delay by 42.64%, 17.39%, and 3.66% in comparison with the shortest path first algorithm (SPF), respectively. |
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| AbstractList | The expansion of megaconstellation networks (MCNs) represents a promising solution for achieving global Internet coverage. To meet the growing demand for satellite services, multipath routing allows the simultaneous establishment of multiple transmission paths, enabling the transmission of flows in parallel. Nevertheless, the mobility of satellites and time-varying link states presents a challenge for the discovery of optimal paths and traffic scheduling in multipath routing. Given the inflexibility of traditional static deep reinforcement learning (DRL)-based routing algorithms in dealing with time-varying constellation topologies, DRL-based multipath routing (DMR) enabled by a graph neural network (GNN) is proposed as a means of enhancing the transmission performance of MCNs. DMR decouples the stochastic optimization problem of multipath routing under traffic and bandwidth constraints into two subproblems: multipath routing discovery and multipath traffic scheduling. Firstly, the minimum hop count-based multipath route discovery algorithm (MHMRD) is proposed for the computation of multiple available paths between all source and destination nodes. Secondly, the GNN-based multipath traffic scheduling scheme (GMTS) is proposed as a means of dynamically scheduling the traffic on each available path for each data stream, based on the state information of ISLs and traffic demand. Simulation results demonstrate that the proposed scheme can be scaled to constellations with different configurations without the necessity for repeated training and enhance the throughput, completion ratio, and delay by 42.64%, 17.39%, and 3.66% in comparison with the shortest path first algorithm (SPF), respectively. |
| Audience | Academic |
| Author | Han, Chi Yu, Ronghuan Xiong, Wei |
| Author_xml | – sequence: 1 givenname: Chi orcidid: 0000-0001-6043-8045 surname: Han fullname: Han, Chi – sequence: 2 givenname: Wei surname: Xiong fullname: Xiong, Wei – sequence: 3 givenname: Ronghuan surname: Yu fullname: Yu, Ronghuan |
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| Cites_doi | 10.1109/TCOMM.2023.3251360 10.1109/TNSM.2022.3198074 10.1109/MWC.2016.1600317WC 10.1109/TC.2017.2709742 10.1109/TAES.2019.2938447 10.1016/j.comcom.2022.09.029 10.1109/ACCESS.2018.2820719 10.1109/CIoT53061.2022.9766635 10.1109/COMST.2020.3028247 10.23919/JCC.2021.10.015 10.1109/TVT.2023.3333848 10.1109/GLOBECOM54140.2023.10436959 10.1109/MNET.2018.1800097 10.1109/TVT.2019.2925187 10.1109/TWC.2022.3144189 10.1109/WCNC55385.2023.10118676 10.1109/MCSA.1999.749281 10.1109/TCOMM.2018.2880785 10.1145/3229607.3229610 10.1109/JSAC.2024.3365878 10.1109/MNET.2018.1800193 10.1109/TVT.2022.3217952 10.1007/s11633-022-1326-3 10.1109/ACCESS.2022.3151081 10.1109/TMC.2018.2831679 10.1109/MNET.012.2300052 10.1016/j.neucom.2022.08.005 10.1109/TCC.2019.2961093 10.1049/iet-com.2016.0574 10.1109/ACCESS.2020.2978582 10.1109/JSAC.2020.3000405 10.3390/rs15082165 10.3390/electronics11182952 10.1109/INFOCOM42981.2021.9488736 10.1109/TNET.2021.3126933 10.1109/TMC.2022.3215976 |
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| Snippet | The expansion of megaconstellation networks (MCNs) represents a promising solution for achieving global Internet coverage. To meet the growing demand for... |
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| SubjectTerms | Algorithms Bandwidths Data transmission Decision making Deep learning Efficiency Graph neural networks Machine learning Network topologies Neural networks Routing (telecommunications) Satellite communications Satellite communications services industry Satellite constellations Scheduling Shortest-path problems Topology Traffic assignment Traffic information |
| Title | Deep Reinforcement Learning-Based Multipath Routing for LEO Megaconstellation Networks |
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