# Hierarchical Resource Management for Mega-LEO Satellite Constellation
## 2 Related Works  
Resources (slots, beams, spectrum, power, etc.) play an important role in communication networks. Resources must be used at the right time and in the right places to fully realize the potential of communication networks, while also minimizing resource consumption. As a result, rational and optimized resource scheduling has become crucial for satellite communication networks, with several significant outcomes presented in recent years. In [5], a multi-layer LEO-MSS (mobile satellite system) architecture has been proposed, along with research on resource optimization to enhance network capacity. A novel hierarchical resource management framework and a joint dynamic radio resource optimization method are presented, demonstrating significant improvements in throughput. A multi-aspect expanded hypergraph (MAEH) method has been proposed to accurately depict the spatio-temporal features of multi-domain resource allocation in satellite networks (SNs) in [6], along with a two-stage scheme to accomplish resource allocation rapidly with low computational complexity and a high resource/utilization ratio. Prediction is another effective approach in resource allocation and it has been used in rentable LEO satellite communication networks [7]. A long short-term memory (LSTM)-based traffic prediction framework and a task-driven joint resource allocation method have been proposed. In [8], the authors conducted research on overcoming problems in resource scheduling for a large number of accessed users and dynamic wireless environments. They proposed an iterative suboptimal algorithm and an enhanced meta-critic learning (EMCL) algorithm. The simulation results verified the effectiveness of these two algorithms. The open environment of satellite communication and the scarcity of spectrum resources make spectrum management an important issue in satellite communication systems. A market-driven spectrum allocation technique has been proposed in [9], and the terrestrial agent serves as a seller of spectrum/band. They designed an optimal incentive model to maximize benefits, improve spectrum efficiency, and reduce the agent costs. A cognitive radio (CR)-based spectrum-sharing approach has been used in spectrum management for the satellite-aerial-terrestrial integrated network (SATIN), and a cooperative beamforming strategy has been used to facilitate secure and energy-efficient IoT communications in [10]. In [11], am elastic resource allocation algorithm based on dispersion degree (ERA-DD) has been proposed to handle routing, wavelength, and slot assignment in the satellite-assisted Internet of Things (IoT) communication system. The traffic blocking rate, wavelength utilization, average communication delay, and average initial delay are evaluated through simulations, demonstrating that the traffic blocking rate can be reduced by 32.5% and wavelength utilization can be increased by 1.6%. To cope with the problem of spectral efficiency, user access, and limited uplink transmission resource allocation in satellite IoT, a novel scheduling algorithm that combines simulated annealing and Monte Carlo (SA-MC) has been proposed in [12] to achieve optimal dynamic scheduling. The authors in [13] also proposed an uplink transmission scheme in ultra-dense LEO satellite networks. This scheme employed a two-timescale resource allocation framework to maximize the overall data rate. The small timescale problem was described as a Markov decision process (MDP)to cope with changes in link states relative to satellite movement and rain attenuation, and the large timescale problem was formulated as an integer programming problem to deal with band switching. In [14], the hybrid beamforming, adaptive user scheduling, and resource allocation framework for the terrestrial-satellite network (TSN) has been proposed to mitigate intra-beam and inter-beam interference. For the multi-beam mega-LEO satellite constellation, mutual interference among satellites and beams limits the implementation of spectra and reduces spectrum efficiency.