一种基于业务量预测的低轨卫星波束动态开关算法

A Dynamic Beam Switching Algorithm for LEO Satellite Based on Communication Traffic Volume Prediction

摘要: 本文针对低轨卫星星座的星上资源浪费、波束开关算法复杂度过高的问题，提出了基于业务量预测的波束动态开关算法。首先，通过建模得到单颗卫星覆盖区域的发射信号强度分布；接着，考虑到通信业务量具有明显的时空周期性，利用长短期记忆神经网络(Conv-LSTM)对地区的通信业务量进行准确预测；最后，根据这两者定义波束业务重要程度，以此选择需要调整的波束。当卫星的服务区域切换时，该算法可依据预测结果对下一时刻的波束状态进行预设，从而减少波束调整次数。仿真结果表明，该算法考虑了波束覆盖区域的信号盲区与地面通信业务量，有效降低波束资源浪费与计算复杂度。

Abstract: This paper proposes a dynamic beam-switching algorithm that utilizes traffic volume prediction to address the high complexity of the current beam-switching algorithm and the wastage of onboard resources in low-orbit satellite constellations. Firstly, we model the signal intensity distribution of a single satellite coverage area. Then, considering the obvious spatiotemporal periodicity of the communication traffic volume, Convolutional Long Short Term Memory neural network (Conv-LSTM) is used to accurately analyze the regional communication traffic volume prediction. Finally, we de-fine the importance of the beam service according to the signal intensity distribution and commu-nication traffic volume, so as to select the beam that needs to be switched off. Based on the proposed algorithm, when the service area of the satellite is switched, the algorithm can preset the beam state at the next moment according to the prediction result, thereby reducing the number of beam adjustments. The simulation results show that the algorithm takes into account the signal blind ar-ea of the beam coverage area and the ground communication traffic, effectively reducing the waste of beam resources and computational complexity.