Satellite Internet of Things Load Estimation and Prediction

MAO Xiwen; WANG Haitao; ZHANG Gengxin

doi:10.13756/j.gtxyj.2024.230013

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Satellite Internet of Things Load Estimation and Prediction

更新时间：2024-10-08

- Satellite Internet of Things Load Estimation and Prediction
- Study on Optical Communications Issue 5, Pages: 23001301-23001306(2024)
- 作者机构：
  
  南京邮电大学　通信与信息工程学院，南京　210003
- 作者简介：
- 基金信息：
- DOI：10.13756/j.gtxyj.2024.230013
  CLC： TN927
- Published：10 October 2024，
  
  Received：02 April 2023，
  
  Revised：06 April 2023，
- 稿件说明：
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茆习文，王海涛，张更新. 卫星物联网负载量估计及预测[J]. 光通信研究，2024(5)：230013.

Mao X W, Wang H T，Zhang G X. Satellite Internet of Things Load Estimation and Prediction[J]. Study on Optical Communications，2024(5)：230013.
茆习文，王海涛，张更新. 卫星物联网负载量估计及预测[J]. 光通信研究，2024(5)：230013. DOI： 10.13756/j.gtxyj.2024.230013.

Mao X W, Wang H T，Zhang G X. Satellite Internet of Things Load Estimation and Prediction[J]. Study on Optical Communications，2024(5)：230013. DOI： 10.13756/j.gtxyj.2024.230013.

摘要

【目的】

随着卫星物联网蓬勃发展，海量短突发的用户加剧了接入网络用户间碰撞和干扰问题，对此，相关组织和个人提出了一些动态接入方案，然而，对于其中大部分方案，都需要知道准确的未来时隙接入申请量。目前，一些文献中已经提出了负载量估计方案，但准确度都不高，且只能实现当前时隙的负载量估计。

【方法】

因此，在现有研究的基础上，文章提出了一种基于前导码状态和参数估计的负载量估计方法和基于机器学习的负载量预测方法。基于前导码状态和参数估计的负载量估计方法通过分析卫星物联网时隙中前导码处于不同状态的概率与当前时隙接入申请数的关系，列出最大似然参数估计表达式，使用最大似然参数估计手段实现当前时隙负载量的估计。基于机器学习的负载量预测方法将负载量估计值作为历史数据，联合使用长短期记忆（LSTM）网络和自回归滑动平均（ARMA）模型，实现对未来时隙负载量的预测。

【结果】

仿真结果表明，基于前导码状态和参数估计的负载量估计方法的估计误差不到1%，以负载量估计结果作为机器学习历史数据的负载量预测的综合误差在6%左右。

【结论】

文章所提的负载量估计及预测方法预测的误差在可接收范围内，可以为动态接入方案提供准确的未来时隙接入申请量。

Abstract

【Objective】

With the rapid development of the satellite Internet of Things (IoT)

a large number of short-burst users are aggravating collisions and interference among users of the access network. To address this issue

several organizations and individuals have put forward some dynamic access schemes. However

for most of the proposed dynamic access schemes

it is necessary to know the exact number of future time slot access applications. At present

some load estimation schemes have been proposed in the literature

but the accuracy of these schemes is not high

and they can only achieve load estimation for current time slot.

【Methods】

To solve this issue

we propose a load estimation method based on the leading code state and parameter estimation. A load prediction method based on machine learning is also proposed. The load estimation method based on leading code status and parameter estimation analyzes the relationship between the probability of leading code in different states within the time slot of the satellite IoT and the number of requests for access to the current time slot. It gives the maximum likelihood parameter estimation expression and uses the maximum likelihood parameter estimation method to estimate the current time slot load. The load prediction method based on machine learning takes the estimated load value as its historical data

combining the Long and Short Term Memory (LSTM) network and the Auto Regressive Moving Average (ARMA) model to predict the future time slot load.

【Results】

The simulation results show that the estimated error of the load estimation method based on leading code state and parameter estimation is less than 1%. The comprehensive error of the load prediction method based on load estimation results as historical machine learning data is about 6%.

【Conclusion】

The predicted error of the proposed load estimation and prediction method is within the acceptable range

thus offering accurate future slot access requests for dynamic access schemes.

关键词

卫星物联网负载预测负载估计机器学习

Keywords

satellite IoTload predictionload estimationmachine learning

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