LEO Satellite Channel Allocation Scheme Based on Reinforcement Learning

Abstract EN

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.