Abstract:
To accommodate the rapid development of the Satellite-Assisted Internet of Things
(S-IoT) and the ever-shifting landscape of future communications, a hybrid satellite
network integrating Geosynchronous Earth Orbit (GEO) and Low Earth Orbit (LEO)
satellites has been proposed. This architecture is gaining prominence as a strong contender
among emerging network architectures. The primary objective of this research
is to optimize the network’s end-to-end energy consumption by addressing the latency
of the network as a whole, cache capability, joint admission control, association, and
power allocation. An equally pivotal facet involves ensuring fairness in IoT device
association and impartial distribution of spectrum resources while simultaneously optimizing
throughput. This distinctive objective, which remains relatively uncharted in
prior research, encompasses the harmonization of fairness considerations alongside the
pursuit of maximal efficiency. However, the hybrid GEO-LEO satellite network faces
significant challenges due to limited onboard communication and computing resources,
particularly in task offloading. The problem is classified as an NP-hard mixed-integer
non-linear programming (MINLP) problem, which demands an effective solution approach
with a low computational load. An outer approximation algorithm (OAA) is
proposed to obtain a near-optimal solution to address this. The results demonstrate the
ability of the approach to achieve fairness in IoT association, fairness in resource block
(RB) allocation, and overall throughput in the hybrid GEO-LEO satellite network. This
research contributes to advancing satellite networks, particularly the hybrid GEO-LEO
architecture, to meet the future communication needs of the S-IoT. The proposed OAA
algorithm provides a practical and efficient solution for the NP-hard MINLP problem,
opening avenues for further advancements in this field.
