Hierarchical adaptive learning and optimization for joint caching and routing in ISL networks

Abstract

With the surge in mobile traffic and the demand for wide coverage, Low-Earth Orbit (LEO) satellite internet has garnered significant attention. However, the limited battery capacity of LEO satellites necessitates efficient energy management. Meanwhile, content caching presents a promising solution to reduce transmission delays and energy consumption of LEO satellites by caching popular files nearby end users. Unlike terrestrial networks, content caching in LEO satellites requires joint optimization with multi-hop routing due to inherent structural differences in ISL (Inter-Satellite Link) networks. In this paper, we formulate an optimization problem aimed at minimizing the average transmission power of LEO satellites while ensuring timely content delivery. To this end, we apply Lyapunov optimization framework to obtain a Hierarchical Caching and Routing (HCR) algorithm where content caching decisions are made over longer periods while routing decisions are made over shorter periods. Finally, via extensive simulations, we demonstrate that the HCR algorithm excels existing algorithms, showing up to an average of 54% and 67% reduction in average queue backlog and average power consumption, respectively.