Cell Clustering Beam Hopping With Interference Avoidance: A CoopMASAC-PSCT Framework

Multi-beam satellites (MBS) exploit multiple spot beams and frequency reuse to achieve high spectral efficiency and flexible coverage, which are key characteristics of modern satellite communication systems. Among them, beam hopping (BH) leverages phased-array antennas to steer onboard beams and employs time-division multiplexing (TDM) to dynamically schedule illumination patterns in the time domain, with electronic beam position switching enabling rapid reconfiguration. However, existing works exhibit two critical limitations: (1) they rely on global decision strategies that incur high inference complexity, which further increases with the number of beams and ground cells, making it difficult to balance performance and computational complexity; (2) in pursuing high spectral reuse, they overlook beam overlap and co-frequency interference (CFI), and thus cannot effectively mitigate interference without sacrificing spectral efficiency. To address these challenges, this paper proposes a cell clustering-based BH (CCBH) algorithm with low complexity and interference avoidance. Specifically, we propose a region-growing cell-clustering method based on user demand load balancing in which each beam independently serves a cell cluster, and full-frequency reuse across beams maximizes spectral efficiency. In addition, based on the centralized training and decentralized execution (CTDE) paradigm, we propose a cooperative multi-agent soft actor–critic (SAC) framework with parameter sharing and centralized training, called CoopMASAC-PSCT. Among them, an SAC agent is deployed for each beam; Specifically, all actor networks share the same architecture and parameters, and the execution phase remains decentralized, with each actor making decisions solely on its local observations; To prevent inter-cluster interference, the shared global reward integrates system throughput, queueing delay fairness, and an interference-penalty term; Moreover, only a single global critic network is employed, which accesses the joint observations and actions of all agents during training, thus balancing individual beam performance with influence between all beams. Simulation and comparative analyses demonstrate that CCBH delivers better performance while significantly reducing inference complexity.