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 pa-rameter 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 maxi-mum 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 da-ta,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 meth-od based on leading code state and parameter estimation is less than 1%.The comprehensive error of the load prediction meth-od based on load estimation results as historical machine learning data is about 6%.[Conclusion]The predicted error of the pro-posed load estimation and prediction method is within the acceptable range,thus offering accurate future slot access requests for dynamic access schemes.
基金项目
国家自然科学基金区域创新发展联合基金重点资助项目(U21A20450)
国家科技期刊平台
NSTL
万方数据