Throughput and Link Utilization Improvement in Satellite Networks: A Learning-Enabled Approach
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866916269766213632 |
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| author | Wu, Hao |
| author_facet | Wu, Hao |
| contents | Satellite networks provide communication services to global users with an uneven geographical distribution. In densely populated regions, Inter-satellite links (ISLs) often experience congestion, blocking traffic from other links and leading to low link utilization and throughput. In such cases, delay-tolerant traffic can be withheld by moving satellites and carried to navigate congested areas, thereby mitigating link congestion in densely populated regions. Through rational store-and-forward decision-making, link utilization and throughput can be improved. Building on this foundation, this letter centers its focus on learning-based decision-making for satellite traffic. First, a link load prediction method based on topology isomorphism is proposed. Then, a Markov decision process (MDP) is formulated to model store-and-forward decision-making. To generate store-and-forward policies, we propose reinforcement learning algorithms based on value iteration and Q-Learning. Simulation results demonstrate that the proposed method improves throughput and link utilization while consuming less than 20$\%$ of the time required by constraint-based routing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_00723 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Throughput and Link Utilization Improvement in Satellite Networks: A Learning-Enabled Approach Wu, Hao Networking and Internet Architecture Systems and Control Satellite networks provide communication services to global users with an uneven geographical distribution. In densely populated regions, Inter-satellite links (ISLs) often experience congestion, blocking traffic from other links and leading to low link utilization and throughput. In such cases, delay-tolerant traffic can be withheld by moving satellites and carried to navigate congested areas, thereby mitigating link congestion in densely populated regions. Through rational store-and-forward decision-making, link utilization and throughput can be improved. Building on this foundation, this letter centers its focus on learning-based decision-making for satellite traffic. First, a link load prediction method based on topology isomorphism is proposed. Then, a Markov decision process (MDP) is formulated to model store-and-forward decision-making. To generate store-and-forward policies, we propose reinforcement learning algorithms based on value iteration and Q-Learning. Simulation results demonstrate that the proposed method improves throughput and link utilization while consuming less than 20$\%$ of the time required by constraint-based routing. |
| title | Throughput and Link Utilization Improvement in Satellite Networks: A Learning-Enabled Approach |
| topic | Networking and Internet Architecture Systems and Control |
| url | https://arxiv.org/abs/2406.00723 |