# Hierarchical Resource Management for Mega-LEO Satellite Constellation
## 2 Related Works
In [29], resource allocation has been considered in LEO multi-beam STNs to serve vehicles, and the authors modeled it as a cooperative multi-agent reinforcement learning process where each beam acts as an agent. Centralized training and distributed execution is performed in the training of agents. Traditional centralized ground control with low invulnerability and large propagation delays cannot adapt to large constellation systems. Hence, distributed resource allocation approaches are preferred, and a lightweight method based on contract theory with on-time interaction has been presented in [30]. To deal with limited power resources and high dynamic characteristics in electromagnetic environments, user distribution and service requirments in LEO satellite communication systems, service, priority, and multi-beam scheduling models have been established in [31]. The authors provided an enhanced artificial bee colony algorithm to improve the ability of quick convergence and global optimization, which has high applicability to the high dynamic characteristics of LEO satellites. The digital video broadcast return channel satellite 2 (DVB-RCS2) standard provides return link specifications for satellite communication. To maximize fairness, a joint superframe and resource allocation problem is decomposed into two-level hierarchical problems, and an iterative algorithm is designed to solve them in [32]. For resource allocation in beam-hopping satellite communication systems, a joint beam-hopping and precoding algorithm has been presented in [33] to achieve resource allocation and intra-cluster interference suppression. The simulation results show that the proposed approach achieves reliable and near-optimal transmission capacity. The authors in [34] studied the combination scheme of beam-hopping and NOMA. They optimized resource allocation by exploiting time-domain flexibility using an optimized BH design and power-domain flexibility using NOMA. In [35], we proposed heuristic and maximum weighted clique (MWC) algorithms to realize the joint scheduling of beams and slots. An auxiliary graph was constructed to find the MWC, where the weights of vertices were assigned while considering not only user/traffic distribution, inter-beam interference, and fairness, but also delay requirements and channel status (path loss and rain attenuation). The simulation results indicate that the approach achieves excellent performance in terms of throughput and degree of user satisfaction. For the interference challenges in multi-LEO constellation coexistence scenarios, the authors in [36] proposed a scheme to tackle this issue through dynamically adjusting the beam bandwidth. In recent years, some new contributions have been proposed for mega/ultra-dense LEO constellations. In [37], a distributed resource management framework (DRMF) was proposed for a malicious jamming environments for the Internet of Satellites (IoS). This framwork was divided into three sub-problems: a traffic prediction problem, an anti-jamming decision problem, and a resource matching problem. The authors in [38] investigated resource scheduling for satellite-air communication in ultra-dense LEO networks and proposed a framework to deal with the high dynamic characteristics of LEO satellites and aircraft. In [39], a downlink resource scheduling algorithm was presented to achieve a fair allocation of time-frequency resources. The available power, beams, and channel resource allocation in multi-LEO constellation networks has been studied in [40] using a hierarchical multi-agent multi-armed bandit resource allocation for LEO constellations (mmRAL). In [41], a mobility management strategy based on reinforcement learning (RL) has been proposed to minimize handovers and migration delays in mega-LEO satellite constellations. A joint optimization problem of computation offloading and a resource allocation algorithm has been proposed in [42] to minimize service delays and energy consumption. Additionally, the absence of ML techniques for resource management in the MLSCS is noteworthy.