Throughput and Link Utilization Improvement in Satellite Networks: A Learning-Enabled Approach
<|reference_start|>Throughput and Link Utilization Improvement in Satellite Networks: A Learning-Enabled Approach: 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.<|reference_end|>
@article{wu2024throughput,
    title={Throughput and Link Utilization Improvement in Satellite Networks: A
  Learning-Enabled Approach},
    author={Hao Wu},
    journal={arXiv preprint arXiv:2406.00723},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.00723},
    primaryClass={cs.NI cs.SY eess.SY}
}