# Hierarchical Resource Management for Mega-LEO Satellite Constellation
## 2 Related Works
To solve this problem, an online frequency allocation algorithm based on machine learning (ML) has been proposed in [15]. Under this algorithm, maximizing throughput while constraining interference can be achieved. In [16], the authors also considered interference management in multi-beam satellite communication systems. They use an inter-beam interference coefficient matrix derived from frequency reuse to measure the level of co-channel interference. A novel joint power and bandwidth allocation algorithm has been proposed. The golden-section theory and subgradient iteration have been used to solve the optimization problem. The numerical results demonstrate that the proposed algorithm could maximize capacity and minimize bandwidth utilization variance. A joint approach to caching, computing, and communication (3C) resource management strategy was presented in [17], and a tapped water-filling algorithm was proposed for the TSN, in which hot air balloons are used as relays between satellites and ground stations. The authors formulated a collaborative resource allocation problem for the TSN and solved it using geometric programming with Taylor series approximation. The 3C resource management model was also studied in [18] to maximize overall system throughput. This problem formulated as a Nash bargaining game and solved by constructing an optimization model. Similarly, resource allocation for the coexistence of satellite and terrestrial wireless communication systems in the same frequency band has been studied in [19; 20], and these studies utilize a radio map (RM) to reduce system overhead. In [21], the authors also provided an efficient power and spectrum allocation scheme to minimize inter-component interference while maximizing throughput provided to users. This scheme consists of two simple linear machine learning-based tools and a linear transformation tool. High-throughput satellites (HTS) are an important composition of future 6G systems. Digital beamforming (DBF) has been proposed, which was combined with power resource allocation to improve the flexibility of HTS systems in [22]. Resource optimization for beamforming (BF) in next-generation satellite communication (SATCOM) with non-orthogonal multiple access (NOMA) has been investigated in [23]. A distributed resource allocation strategy was proposed for BF and payload power resource allocation, and an efficient user scheduling scheme was designed to improve system throughput. The authors in [24] also studied a soft-defined approach to jointly manage networking, caching, and computing resources in satellite-terrestrial networks (STNs), formulating it into a joint optimization problem which they solved with Q-learning. It has been shown that the performance of STNs can be improved significantly through the joint allocation of networking, caching, and computing resources. The deep reinforcement learning (DRL) method has also been used in resource allocation for STNs. The authors formulated the optimization problem as a Markov decision process to minimize long-term costs in terms of a trade-off between task execution latency and energy consumption [25]. A DRL-based band allocation method in multi-beam satellite communication systems has been proposed in [26] to enhance transmission efficiency with low complexity. In [27], a dynamic user association (DUA) mechanism with task classification was proposed to meet the requirements of load balancing and user task processing in STNs, and a dynamic cell range extension algorithm was developed to adjust the load in terms of resilient backhaul capacity. Furthermore, CR was introduced in frequency reuse and interference management in STNs in [28]. The authors formulated the spectrum optimization problem into a tractable convex optimization form and proposed a closed-form expression for both power allocation and subchannel allocation.