卫星物联网负载量估计及预测

茆习文; 王海涛; 张更新

doi:10.13756/j.gtxyj.2024.230013

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卫星物联网负载量估计及预测

无线通信技术 | 更新时间：2024-10-08

- 卫星物联网负载量估计及预测
- Satellite Internet of Things Load Estimation and Prediction
- 光通信研究 2024年第5期页码：23001301-23001306
- 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：
  
  茆习文（1998－），男，安徽宣城人。硕士，主要研究方向为卫星物联网。
  王海涛，副研究员。E-mail：enterescf2@njupt.edu.cn。
- 基金信息：
  
  国家自然科学基金资助项目(U21A20450)
- DOI：10.13756/j.gtxyj.2024.230013
  中图分类号： TN927
- 纸质出版日期：2024-10-10，
  
  收稿日期：2023-04-02，
  
  修回日期：2023-04-06，
- 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

references

于浩淼. 物联网发展及应用前景分析[J]. 通信电源技术, 2019, 36(11): 142-144.

Yü H M. Analysis of Development and Application Prospect of the Internet of Things[J]. Telecom Power Technology, 2019, 36(11): 142-144.

华为技术有限公司. 2016年全球联接指数(GCI)[EB/OL](2016-04-11)[2023-04-01].https://www.huawei.com/minisite/gci/assets/files/gci_2016_whitepaper_cn.pdf?v=20191122https://www.huawei.com/minisite/gci/assets/files/gci_2016_whitepaper_cn.pdf?v=20191122.

Huawei Technologies Co., Ltd., 2016 Global Connectivity Index (GCI)[EB/OL](2016-04-11)[2023-04-01].https://www.huawei.com/minisite/gci/assets/files/gci_2016_whitepaper_cn.pdf?v=20191122https://www.huawei.com/minisite/gci/assets/files/gci_2016_whitepaper_cn.pdf?v=20191122.

沈俊, 高卫斌, 张更新. 低轨卫星物联网的发展背景、业务特点和技术挑战[J]. 电信科学, 2019, 35(5): 113-119.

Shen J, Gao W B, Zhang G X. Developing Background, Service Characteristics and Challenges of LEO IoT[J]. Telecommunications Science, 2019, 35(5): 113-119.

程一凡, 曲至诚, 张更新. 低轨卫星星座物联网业务量建模[J]. 电子与信息学报, 2021, 43(4): 1050-1056.

Cheng Y F, Qu Z C, Zhang G X. Traffic Modeling for Low Earth Orbit Satellite Constellation Internet of Things[J]. Journal of Electronics & Information Technology, 2021, 43(4): 1050-1056.

丁晓进, 洪涛, 刘锐, 等. 低轨卫星物联网体系架构及关键技术研究[J]. 天地一体化信息网络, 2021, 2(4): 10-18.

Ding X J, Hong T, Liu R, et al. Research on Architecture of LEO Satellite Internet of Things and Key Technologies[J]. Space-Integrated-Ground Information Networks, 2021, 2(4): 10-18.

张更新, 揭晓, 曲至诚. 低轨卫星物联网的发展现状及面临的挑战[J]. 物联网学报, 2017, 1(3): 6-9.

Zhang G X, Jie X, Qu Z C. Development Status and Challenges of LEO IoT[J]. Chinese Journal on Internet of Things, 2017, 1(3): 6-9.

张德鹏, 俞研, 王悦. 卫星激光网络应用技术研究[J]. 光通信研究, 2023(1): 32-41.

Zhang D P, Yü Y, Wang Y. Research on Application Technology of Satellite Laser Network[J]. Study on Optical Communications, 2023(1): 32-41.

ZTE Comporation. MTC Simulation Results with Specific Solutions [DB/OL](2010-08-15)[2023-04-01].https://www.3gpp.org/ftp/tsg_ran/WG2_RL2/TSGR2-7//Docs/R2-104662.ziphttps://www.3gpp.org/ftp/tsg_ran/WG2_RL2/TSGR2-7//Docs/R2-104662.zip.

3GPP.TR 37.868-2011, Study on RAN Iimprovements for Machine-type Communication[S].

Liu X, Sun Q, Lu W, et al. Big-data-based Intelligent Spectrum Sensing for Heterogeneous Spectrum Communications in 5G[J]. IEEE Wireless Communications, 2020, 27(5): 67-73.

Yü L, Guo Y, Wang Q, et al. Spectrum Availability Prediction for Cognitive Radio Communications: a DCG Approach[J]. IEEE Transactions on Cognitive Communications and Networking, 2020, 6(2): 476-485.

Hochreiter S, Schmidhuber J. Long Short-term Memory[J]. Neural Computation, 1997, 9(8): 1735-1780.

魏迪, 曾海彬, 洪锋, 等. 基于LSTM网络和特征融合的通信干扰识别[J]. 电讯技术, 2022, 62(4): 450-456.

Wei D, Zeng H B, Hong F, et al. Communication Jamming Signals Recognition based on LSTM Network and Feature Fusion[J]. Telecommunication Engineering, 2022, 62(4): 450-456.

Gong M, Liu J, Qin A K, et al. Evolving Deep Neural Networks via Cooperative Coevolution with Backpropagation[J]. IEEE Transactions on Neural Networks and Learning Systems, 2021, 32(1): 420-434.

梁昌勇, 马银超, 陈荣, 等. 基于SVR-ARMA组合模型的日旅游需求预测[J]. 管理工程学报, 2015, 29(1): 122-127.

Liang C Y, Ma Y C, Chen R, et al. The Daily Forecasting Tourism Demand based on SVR-ARMA Combination Model[J]. Journal of Industrial Engineering and Engineering Management, 2015, 29(1): 122-127.

杨高飞, 徐睿, 秦鸣, 等. 基于ARMA和卡尔曼滤波的短时交通预测[J]. 郑州大学学报(工学版), 2017, 38(2): 36-40.

Yang G F, Xu R, Qin M, et al. Short-term Traffic Volume Forecasting based on ARMA and Kalman Filter[J]. Journal of Zhengzhou University (Engineering Science), 2017, 38(2): 36-40.

朱振宇, 朱晓荣, 蔡艳, 等. 基于时隙ALOHA与自适应接入类禁止混合的大规模终端接入算法[J]. 物联网学报, 2021, 5(1): 90-98.

Zhu Z Y, Zhu X R, Cai Y, et al. Large-scale Terminal Access Algorithm based on Slot ALOHA and Adaptive Access Class Barring[J]. Chinese Journal on Internet of Things, 2021, 5(1): 90-98.

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