# Hierarchical Resource Management for Mega-LEO Satellite Constellation
## 2 Related Works
The survey in [43] presented the use of ML for radio resource management in mega constellations. In [44], the authors presented a hierarchical and sub-area network control architecture based on the typical characteristics of LEO giant constellations. All the above works are presented in Table 1. The existing literature on resource management in satellite communication systems or SINs primarily focuses on specific scenarios and the allocation of individual resources. However, there is a lack of research and consideration for a comprehensive network resource management framework for the MLSCS. Therefore, this paper aims to propose a unified resource management architecture to enhance the efficiency of deploying and operating the MLSCS. \begin{table}
\begin{tabular}{c c c c c} \hline
**Reference** & **Task** & **Approach** & **Drawback** & **Application** \\ \hline
[5] (2020) & Resource allocation & Lagrange dual method & The approach relies on high-altitude platforms (HAPs) and terrestrial relays (TRs) & Simulated environments \\
[8] (2022) & Resource allocation & EMCL & Not LEO constellation & Simulated environments \\
[13] (2022) & Resource allocation & MDP and DRL & Only uplink transmission in LEO networks with multiband antennas & Simulated environments \\
[15] (2022) & Interference control & Online frequency allocation & Downlink spectrum sharing in a two-satellite system & Simulated environments \\
[17; 18; 19; 20; 21] & 3C resource management & Tapped water-filling algorithm, Nash bargaining game and resource map & The optimization target is throughput without considering other factors & Simulated environments \\
[24; 25; 26; 27; 28; 29] & Resource allocation & Q-learning, DRL, and CR & The emphasis is on joint allocation in TSNs & Simulated environments \\
[2019; 2022] & Resource allocation & Contract theory, artificial bee colony algorithm, and iterative algorithm & No consideration of mega-LEO constellation and the optimization target is single & Simulated environments \\
[33; 34; 35; 36] & Resource allocation & Precoding algorithm and MWC & The focus is on beam-hopping scheduling & Simulated environments \\
[2021] & Resource allocation & Traffic prediction (TP) and resource matching (RM) & Only consider the regional scenario & Simulated environments \\
[37] (2021) & Resource allocation & Lyapunov optimization and successive convex approximation & Focus on satellite-air communications & Simulated environments \\
[38] (2023) & Resource allocation & Distributed algorithm & Only considers the downlink & Simulated environments \\
[40] (2023) & Resource allocation & Multi-agent multi-armed bandit & Two-way transmission in satellite communication systems may be infeasible sometimes & Simulated environments \\
[41] (2023) & Resource allocation & RL & Focus on mobility management & Simulated environments \\
[42] (2024) & Resource allocation & Breadth-first search (BFS) & Focus on edge computation and computation offloading & Simulated environments \\
[43] (2023) & Resource allocation & ML & Focus on artificial intelligence for satellite communication & Analysis \\
[44] (2024) & Network control & Design & Focus on the control of LEO giant & Simulated environments \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of state-of-the-art approaches for resource allocation in terms of performed tasks, approaches, drawbacks, and applications.