The mega-low Earth orbit (LEO) satellite constellation is pivotal for the future of satellite Internet and 6G networks. In the mega-LEO satellite constellation system (MLSCS), which is the spatial distribution of satellites, global users, and their services, along with the utilization of global spectrum resources, significantly impacts resource allocation and scheduling. This paper addresses the challenge of effectively allocating system resources based on service and resource distribution, particularly in hotspot areas where user demand is concentrated, to enhance resource utilization efficiency. We propose a novel three-layer management architecture designed to implement scheduling strategies and alleviate the processing burden on the terrestrial Network Control Center (NCC), while providing real-time scheduling capabilities to adapt to rapid changes in network topology, resource distribution, and service requirements. The three layers of the resource management architecture—NCC, space base station (SBS), and user terminal (UT)—are discussed in detail, along with the functions and responsibilities of each layer. Additionally, we explore various resource scheduling strategies, approaches, and algorithms, including spectrum cognition, interference coordination, beam scheduling, multi-satellite collaboration, and random access. Simulations demonstrate the effectiveness of the proposed approaches and algorithms, indicating significant improvements in resource management in the MLSCS.

In recent years, low-orbit satellites have become an important development direction in satellite IoT systems. The number of terminals is large and data collisions occur frequently in the low-orbit satellite IoT scenario. How to design a reliable random access protocol to improve the tolerance of the system for collision is one of the research hotspots in this field. In this paper, the random access protocol, used in the Internet of Things (IoT), for low-orbit satellites is studied, and the access process of the IoT terminals in the scenario is constructed. The access performance of the SSA protocol is analyzed and an improved SSA random access strategy, called Retransmission-SSA (R-SSA), is proposed. The simulation results show that the designed R-SSA can effectively tolerate the signal conflicts between terminals in the beam-hopping LEO IoT scenario and improve the probability of the concurrent access of low-orbit sIoT terminals.

The Mega Low Earth Orbit (LEO) satellite constellation is pivotal for the future of satellite Internet and 6G networks. In the Mega LEO Satellite Constellation System (MLSCS), the spatial distribution of satellites, global users, and their services, along with the utilization of global spectrum resources, significantly impacts resource allocation and scheduling. This paper addresses the challenge of effectively allocating system resources based on service and resource distribution, particularly in hotspot areas where user demand is concentrated, to enhance resource utilization efficiency. We propose a novel three-layer management architecture designed to implement scheduling strategies and alleviate the processing burden on the terrestrial Network Control Center (NCC), while providing real-time scheduling capabilities to adapt to rapid changes in network topology, resource distribution, and service requirements. The three layers of the resource management architecture—NCC, Space Base Station (SBS), and User Terminal (UT)—are discussed in detail, along with the functions and responsibilities of each layer. Additionally, we explore various resource scheduling strategies, approaches, and algorithms, including spectrum cognition, interference coordination, beam scheduling, multi-satellite collaboration, and random access. Simulations demonstrate the effectiveness of the proposed approaches and algorithms, indicating significant improvements in resource management within the MLSCS.

Beam Hopping (BH) technology is adopted in satellite communication systems primarily to address the rigid resource allocation issue of traditional fixed beam coverage. By dynamically adjusting beam pointing and dwell time, it achieves efficient utilization and flexible allocation of resources. This paper addresses the beam hopping scheduling problem in Low-Earth-Orbit (LEO) satellite networks for high-priority and time-sensitive services. We establish a system gain maximization model and propose a heuristic beam scheduling algorithm (HBSA) that pre-schedules critical traffic and greedily allocates remaining resources under interference and fairness constraints. We further extend the framework with a graph reinforcement learning beam-hopping scheduling scheme (GRL-BHS) that captures structural network dependencies for near-optimal adaptive scheduling. Extensive simulations against seven benchmarks demonstrate that HBSA delivers near-optimal gain (within 2% of MWC and GRL-BHS), ensures high-priority satisfaction, maintains a Jain fairness index above 0.84 under heavy load, and reduces signaling overhead by an order of magnitude, while preserving a low polynomial complexity of O(T × J × K). GRL-BHS offers the highest gain at the cost of offline training. HBSA thus emerges as the most balanced and practically deployable solution, providing a solid foundation for future intelligent BH scheduling in mega constellations.

Beam-Hopping (BH) dynamically adjusts beam pointing and dwell time to overcome the rigid resource allocation of traditional fixed beam coverage. This paper addresses the BH scheduling problem in low-Earth-orbit (LEO) satellite networks for high-priority and time-sensitive services. A system-gain maximization model and a heuristic beam scheduling algorithm (HBSA) that pre-schedules critical traffic and greedily allocates remaining resources under interference and fairness constraints are proposed. Furthermore, this paper extends the framework with a graph reinforcement learning beam-hopping scheduling scheme (GRL-BHS) that captures structural network dependencies for near-optimal adaptive scheduling. Simulations against seven benchmarks show that the HBSA achieves near-optimal gain (within $2\%$ of MWC and GRL-BHS), ensures high-priority satisfaction, maintains a Jain fairness index above $0.84$ under heavy load, and reduces signaling overhead by an order of magnitude, while preserving a low polynomial complexity of $O(T\times J\times K+T\times K^2)$. GRL-BHS offers the highest gain at the cost of offline training, whereas HBSA is the most balanced and practically deployable solution, providing a solid foundation for future intelligent BH scheduling in mega constellations.