LEO Satellite Channel Allocation Scheme Based on Reinforcement Learning

Fei Zheng,Zou Zhou,Zhao Pi,Kaixuan Wang,Chi-Hua Chen

doi:10.1155/2020/8868888

Abstract

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.

Highlights

In recent years, with the development of wireless communication technology, the terrestrial cellular network is facing the explosive growth of data traffic
Dynamic channel allocation (DCA) can realize resource cross-beam scheduling and has a higher resource utilization rate than fixed channel allocation (FCA) [8]. e business request is a discrete dynamic process in communication networks, and the allocation results at the current time will affect the decision at a subsequent time. e existing dynamic channel allocation algorithms focus on the instantaneous performance of the Low Earth Orbit (LEO) satellite system and ignore the time-domain relevance problem in the channel allocation process [7]
Is paper considers the difference in service distribution and the time correlation of channel allocation in satellite communication systems. e Q-Learning algorithm is used for dynamic channel allocation in a LEO satellite. e main contributions are as follows: (i) e on-board resource pool is introduced to manage channel resources in the LEO satellite network. e resource pool integrates information processing, resource allocation and resource acquisition, enabling cross-beam scheduling of channels

Summary

Introduction

With the development of wireless communication technology, the terrestrial cellular network is facing the explosive growth of data traffic. An efficient network resource allocation scheme is urgently needed to solve the above problem in the satellite communication systems. E business request is a discrete dynamic process in communication networks, and the allocation results at the current time will affect the decision at a subsequent time. E existing dynamic channel allocation algorithms focus on the instantaneous performance of the LEO satellite system and ignore the time-domain relevance problem in the channel allocation process [7]. Is paper considers the difference in service distribution and the time correlation of channel allocation in satellite communication systems. E Q-Learning algorithm is used for dynamic channel allocation in a LEO satellite.

Related Work

System Model

Simulation Results and Discussions