Overview of Beam Hopping Algorithms in Large Scale LEO Satellite Constellation

Beam Hopping (BH) technique is the key to improve system throughput and match the user demands in large scale low earth orbit (LEO) satellite constellation network (LS-LEOSCN). In this paper, the beam hopping (BH) algorithms of LS-LEOSCN are overviewed. Firstly, we described the system model on LS-LEOSCN and formulated the BH problem, considering the beam coverage, user distribution, traffic flow and channel capacity model. Secondly, the meta-heuristic based, deep reinforcement learning (DRL) based and hybrid BH algorithms are summarized. Finally, the simulation is conducted to compare different BH algorithms in LS-LEOSCN. Simulation results show that the hybrid algorithms such as PSO-GA perform better than traditional GA or SA based BH algorithms.