Delay, cost, and loss are low in Low Earth Orbit (LEO) satellite networks, which play a pivotal role in channel allocation in global mobile communication system. Due to nonuniform distribution of users, the existing channel allocation schemes cannot adapt to load differences between beams. On the basis of the satellite resource pool, this paper proposes a network architecture of LEO satellite that utilizes a centralized resource pool and designs a combination allocation of fixed channel preallocation and dynamic channel scheduling. The dynamic channel scheduling can allocate or recycle free channels according to service requirements. The Q-Learning algorithm in reinforcement learning meets channel requirements between beams. Furthermore, the exponential gradient descent and information intensity updating accelerate the convergence speed of the Q-Learning algorithm. The simulation results show that the proposed scheme improves the system supply-demand ratio by 14%, compared with the fixed channel allocation (FCA) scheme and by 18%, compared with the Lagrange algorithm channel allocation (LACA) scheme. The results also demonstrate that our allocation scheme can exploit channel resources effectively.

中文翻译：

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基于强化学习的LEO卫星信道分配方案

在低地球轨道（LEO）卫星网络中，延迟，成本和损耗都很低，在全球移动通信系统的信道分配中起着关键作用。由于用户的分配不均匀，现有的信道分配方案无法适应波束之间的负载差异。在卫星资源池的基础上，提出了利用集中式资源池的LEO卫星网络架构，设计了固定信道预分配和动态信道调度的组合分配方法。动态信道调度可以根据服务需求分配或回收空闲信道。强化学习中的Q学习算法可满足梁之间的通道要求。此外，指数梯度下降和信息强度更新加快了Q学习算法的收敛速度。仿真结果表明，与固定信道分配（FCA）方案相比，该方案将系统的供需比提高了14％，与拉格朗日算法信道分配（LACA）方案相比，将其提高了18％。结果还表明，我们的分配方案可以有效地利用信道资源。