Throughput and Link Utilization Improvement in Satellite Networks: A Learning-Enabled Approach

TL;DR

This paper introduces a learning-based approach to improve throughput and link utilization in satellite networks by predicting link loads and optimizing store-and-forward decisions using reinforcement learning.

Contribution

It proposes a novel link load prediction method and reinforcement learning algorithms for store-and-forward decision-making in satellite networks, enhancing performance.

Findings

Significant throughput and link utilization improvements.

Reduced decision-making time compared to constraint-based routing.

Effective prediction of link loads using topology isomorphism.

Abstract

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.