Low Earth orbit(LEO)satellite networks exhibit distinct characteristics,e.g.,limited resources of individual satellite nodes and dynamic network topology,which have brought many challenges for routing algorithms.To sa...Low Earth orbit(LEO)satellite networks exhibit distinct characteristics,e.g.,limited resources of individual satellite nodes and dynamic network topology,which have brought many challenges for routing algorithms.To satisfy quality of service(QoS)requirements of various users,it is critical to research efficient routing strategies to fully utilize satellite resources.This paper proposes a multi-QoS information optimized routing algorithm based on reinforcement learning for LEO satellite networks,which guarantees high level assurance demand services to be prioritized under limited satellite resources while considering the load balancing performance of the satellite networks for low level assurance demand services to ensure the full and effective utilization of satellite resources.An auxiliary path search algorithm is proposed to accelerate the convergence of satellite routing algorithm.Simulation results show that the generated routing strategy can timely process and fully meet the QoS demands of high assurance services while effectively improving the load balancing performance of the link.展开更多
By deploying the ubiquitous and reliable coverage of low Earth orbit(LEO)satellite networks using optical inter satel-lite link(OISL),computation offloading services can be provided for any users without proximal serv...By deploying the ubiquitous and reliable coverage of low Earth orbit(LEO)satellite networks using optical inter satel-lite link(OISL),computation offloading services can be provided for any users without proximal servers,while the resource limita-tion of both computation and storage on satellites is the impor-tant factor affecting the maximum task completion time.In this paper,we study a delay-optimal multi-satellite collaborative computation offloading scheme that allows satellites to actively migrate tasks among themselves by employing the high-speed OISLs,such that tasks with long queuing delay will be served as quickly as possible by utilizing idle computation resources in the neighborhood.To satisfy the delay requirement of delay-sensi-tive task,we first propose a deadline-aware task scheduling scheme in which a priority model is constructed to sort the order of tasks being served based on its deadline,and then a delay-optimal collaborative offloading scheme is derived such that the tasks which cannot be completed locally can be migrated to other idle satellites.Simulation results demonstrate the effective-ness of our multi-satellite collaborative computation offloading strategy in reducing task complement time and improving resource utilization of the LEO satellite network.展开更多
Survivability is used to evaluate the ability of the satellite to complete the mission after failure,while the duration of maintaining performance is often ignored.An effective backup strategy can restore the constell...Survivability is used to evaluate the ability of the satellite to complete the mission after failure,while the duration of maintaining performance is often ignored.An effective backup strategy can restore the constellation performance timely,and maintain good network communication performance in case of satellite failure.From the perspective of network utility,the low Earth orbit(LEO)satellite constellation survivable graphical eva-luation and review technology(GERT)network with backup satel-lites is constructed.A network utility transfer function algorithm based on moment generating function and Mason formula is proposed,the network survivability evaluation models of on-orbit backup strategy and ground backup strategy are established.The survivable GERT model can deduce the expected mainte-nance time of LEO satellite constellation under different fault states and the network utility generated during the state mainte-nance period.The case analysis shows that the proposed surviv-able GERT model can consider the satellite failure rate,backup satellite replacement rate,maneuver control replacement ability and life requirement,and effectively determine the optimal sur-vivable backup strategy for LEO satellite constellation with limi-ted resources according to the expected network utility.展开更多
According to low earth orbit（LEO） satellite systems with users of different levels, a dynamic channel reservation scheme based on priorities is proposed. Dynamic calculation of the thresholds for reserved channels i...According to low earth orbit（LEO） satellite systems with users of different levels, a dynamic channel reservation scheme based on priorities is proposed. Dynamic calculation of the thresholds for reserved channels is the key of this strategy. In order to obtain the optimal thresholds, the traffic is predicted based on the high-speed deterministic movement property of LEO satellites firstly. Then, a channel allocation model based on Markov is established. Finally, the solution of the model is obtained based on the genetic algorithm. Without user location, this strategy effectively reduces handover failures and improves channel utilization by adjusting dynamically the thresholds according to traffic conditions. The simulation results show that the system＇s overall quality of service can be improved by this strategy.展开更多
In the design problem of low earth orbit(LEO) reconnaissance satellite constellation, optimization of coverage performance is the design goal in most current methods. However,in the using process, the user only concer...In the design problem of low earth orbit(LEO) reconnaissance satellite constellation, optimization of coverage performance is the design goal in most current methods. However,in the using process, the user only concerns with the detection capabilities rather than coverage performance. To establish the relationship between these two aspects, the reconnaissance processes of normal stochastic targets are considered and the mathematic models of detection processes are built. The indicators of coverage performance are used to evaluate the detection probability and expectation of detection time delay, which are important factors in reconnaissance constellation estimation viewed from military intelligence discipline. The conclusions confirmed by the final simulation will be useful in LEO reconnaissance constellation design, optimization and evaluation.展开更多
This paper addresses the problem of sensor search scheduling in the complicated space environment faced by the low-earth orbit constellation.Several search scheduling methods based on the commonly used information gai...This paper addresses the problem of sensor search scheduling in the complicated space environment faced by the low-earth orbit constellation.Several search scheduling methods based on the commonly used information gain are compared via simulations first.Then a novel search scheduling method in the scenarios of uncertainty observation is proposed based on the global Shannon information gain and beta density based uncertainty model.Simulation results indicate that the beta density model serves a good option for solving the problem of target acquisition in the complicated space environments.展开更多
In low Earth orbit(LEO)satellite networks,on-board energy resources of each satellite are extremely limited.And with the increase of the node number and the traffic transmis-sion pressure,the energy consumption in the...In low Earth orbit(LEO)satellite networks,on-board energy resources of each satellite are extremely limited.And with the increase of the node number and the traffic transmis-sion pressure,the energy consumption in the networks presents uneven distribution.To achieve energy balance in networks,an energy consumption balancing optimization algorithm of LEO networks based on distance energy factor(DEF)is proposed.The DEF is defined as the function of the inter-satellite link dis-tance and the cumulative network energy consumption ratio.According to the minimum sum of DEF on inter-satellite links,an energy consumption balancing algorithm based on DEF is pro-posed,which can realize dynamic traffic transmission optimiza-tion of multiple traffic services.It can effectively reduce the energy consumption pressure of core nodes with high energy consumption in the network,make full use of idle nodes with low energy consumption,and optimize the energy consumption dis-tribution of the whole network according to the continuous itera-tions of each traffic service flow.Simulation results show that,compared with the traditional shortest path algorithm,the pro-posed method can improve the balancing performance of nodes by 75%under certain traffic pressure,and realize the optimiza-tion of energy consumption balancing of the whole network.展开更多
针对低轨卫星星地通信高动态信道特点,采用正交时频空(Orthogonal Time Frequency Space, OTFS)调制方式,提出一种低导频开销、高精度的两阶段信道估计方法,实现对时延、多普勒频移和信道增益3个参数的精细估计。所提TP-CSIE(Two Phase ...针对低轨卫星星地通信高动态信道特点,采用正交时频空(Orthogonal Time Frequency Space, OTFS)调制方式,提出一种低导频开销、高精度的两阶段信道估计方法,实现对时延、多普勒频移和信道增益3个参数的精细估计。所提TP-CSIE(Two Phase Channel State Information Estimation)方案采用时域训练序列为导频结构,解决时延-多普勒(Delay-Doppler, DD)域嵌入式导频方案在高动态星地链路下导频开销过大的问题。由于DD域信道的固有稀疏性,OTFS信道估计问题被转化为稀疏信号的恢复问题。在算法第一阶段,选用稀疏信号恢复算法进行信道参数的初始估计,利用重叠相加法获得部分先验信息以提高压缩采样匹配追踪(Compressive Sampling Matching Pursuit, CoSAMP)算法的准确性。在算法第二阶段,设计增强型旋转不变子空间算法实现信道参数的准确估计。仿真结果表明,与现有方案相比,所提算法归一化均方误差性能约有7 dB性能的提升,误码率性能约有10 dB的提升。展开更多
针对低轨卫星通信场景下长传播时延导致的信道状态信息(Channel State Information,CSI)过时问题,提出了一种基于地理环境认知的低轨卫星空时多维信道预测方法。首先,通过射线追踪法建模卫星信道,进而确定不同地理环境下影响CSI变化的...针对低轨卫星通信场景下长传播时延导致的信道状态信息(Channel State Information,CSI)过时问题,提出了一种基于地理环境认知的低轨卫星空时多维信道预测方法。首先,通过射线追踪法建模卫星信道,进而确定不同地理环境下影响CSI变化的几个关键因素并将其与信道特征参量建立映射关系;然后,设计了一个由卷积神经网络(Convolutional Neural Network,CNN)和长短时记忆(Long⁃Short Term Memory,LSTM)神经网络构成的组合神经网络模型来有效预测CSI,通过CNN网络提取CSI和特征参量之间变化的空时相关性来认知地理环境对CSI变化的影响,利用LSTM网络处理时间序列的特点根据当前输入信息预测未来某一时刻CSI值。在此基础上,进一步提出了一种离线训练-模型更新-在线预测的实施框架以解决低轨卫星平台资源受限及高动态的问题。仿真结果表明,相较于传统的基于LSTM网络的低轨卫星信道预测方法,所提方法能够有效提升CSI预测精度及其预测模型的稳定性。展开更多
基金National Key Research and Development Program(2021YFB2900604)。
文摘Low Earth orbit(LEO)satellite networks exhibit distinct characteristics,e.g.,limited resources of individual satellite nodes and dynamic network topology,which have brought many challenges for routing algorithms.To satisfy quality of service(QoS)requirements of various users,it is critical to research efficient routing strategies to fully utilize satellite resources.This paper proposes a multi-QoS information optimized routing algorithm based on reinforcement learning for LEO satellite networks,which guarantees high level assurance demand services to be prioritized under limited satellite resources while considering the load balancing performance of the satellite networks for low level assurance demand services to ensure the full and effective utilization of satellite resources.An auxiliary path search algorithm is proposed to accelerate the convergence of satellite routing algorithm.Simulation results show that the generated routing strategy can timely process and fully meet the QoS demands of high assurance services while effectively improving the load balancing performance of the link.
基金This work was supported by the National Key Research and Development Program of China(2021YFB2900600)the National Natural Science Foundation of China(61971041+2 种基金62001027)the Beijing Natural Science Foundation(M22001)the Technological Innovation Program of Beijing Institute of Technology(2022CX01027).
文摘By deploying the ubiquitous and reliable coverage of low Earth orbit(LEO)satellite networks using optical inter satel-lite link(OISL),computation offloading services can be provided for any users without proximal servers,while the resource limita-tion of both computation and storage on satellites is the impor-tant factor affecting the maximum task completion time.In this paper,we study a delay-optimal multi-satellite collaborative computation offloading scheme that allows satellites to actively migrate tasks among themselves by employing the high-speed OISLs,such that tasks with long queuing delay will be served as quickly as possible by utilizing idle computation resources in the neighborhood.To satisfy the delay requirement of delay-sensi-tive task,we first propose a deadline-aware task scheduling scheme in which a priority model is constructed to sort the order of tasks being served based on its deadline,and then a delay-optimal collaborative offloading scheme is derived such that the tasks which cannot be completed locally can be migrated to other idle satellites.Simulation results demonstrate the effective-ness of our multi-satellite collaborative computation offloading strategy in reducing task complement time and improving resource utilization of the LEO satellite network.
基金This work was supported by the National Natural Science Foundation of China(72271124,52232014,72071111,71801127,71671091).
文摘Survivability is used to evaluate the ability of the satellite to complete the mission after failure,while the duration of maintaining performance is often ignored.An effective backup strategy can restore the constellation performance timely,and maintain good network communication performance in case of satellite failure.From the perspective of network utility,the low Earth orbit(LEO)satellite constellation survivable graphical eva-luation and review technology(GERT)network with backup satel-lites is constructed.A network utility transfer function algorithm based on moment generating function and Mason formula is proposed,the network survivability evaluation models of on-orbit backup strategy and ground backup strategy are established.The survivable GERT model can deduce the expected mainte-nance time of LEO satellite constellation under different fault states and the network utility generated during the state mainte-nance period.The case analysis shows that the proposed surviv-able GERT model can consider the satellite failure rate,backup satellite replacement rate,maneuver control replacement ability and life requirement,and effectively determine the optimal sur-vivable backup strategy for LEO satellite constellation with limi-ted resources according to the expected network utility.
基金supported by the National Natural Science Foundation of China(7130108161373137)+1 种基金the Natural Science Foundation of Jiangsu Province(BK20130877BK2012833)
文摘According to low earth orbit（LEO） satellite systems with users of different levels, a dynamic channel reservation scheme based on priorities is proposed. Dynamic calculation of the thresholds for reserved channels is the key of this strategy. In order to obtain the optimal thresholds, the traffic is predicted based on the high-speed deterministic movement property of LEO satellites firstly. Then, a channel allocation model based on Markov is established. Finally, the solution of the model is obtained based on the genetic algorithm. Without user location, this strategy effectively reduces handover failures and improves channel utilization by adjusting dynamically the thresholds according to traffic conditions. The simulation results show that the system＇s overall quality of service can be improved by this strategy.
文摘In the design problem of low earth orbit(LEO) reconnaissance satellite constellation, optimization of coverage performance is the design goal in most current methods. However,in the using process, the user only concerns with the detection capabilities rather than coverage performance. To establish the relationship between these two aspects, the reconnaissance processes of normal stochastic targets are considered and the mathematic models of detection processes are built. The indicators of coverage performance are used to evaluate the detection probability and expectation of detection time delay, which are important factors in reconnaissance constellation estimation viewed from military intelligence discipline. The conclusions confirmed by the final simulation will be useful in LEO reconnaissance constellation design, optimization and evaluation.
基金supported by the National Defense Pre-research Foundation (9140A21041110KG0148)
文摘This paper addresses the problem of sensor search scheduling in the complicated space environment faced by the low-earth orbit constellation.Several search scheduling methods based on the commonly used information gain are compared via simulations first.Then a novel search scheduling method in the scenarios of uncertainty observation is proposed based on the global Shannon information gain and beta density based uncertainty model.Simulation results indicate that the beta density model serves a good option for solving the problem of target acquisition in the complicated space environments.
基金supported by the National Key Research and Development Program(2021YFB2900604).
文摘In low Earth orbit(LEO)satellite networks,on-board energy resources of each satellite are extremely limited.And with the increase of the node number and the traffic transmis-sion pressure,the energy consumption in the networks presents uneven distribution.To achieve energy balance in networks,an energy consumption balancing optimization algorithm of LEO networks based on distance energy factor(DEF)is proposed.The DEF is defined as the function of the inter-satellite link dis-tance and the cumulative network energy consumption ratio.According to the minimum sum of DEF on inter-satellite links,an energy consumption balancing algorithm based on DEF is pro-posed,which can realize dynamic traffic transmission optimiza-tion of multiple traffic services.It can effectively reduce the energy consumption pressure of core nodes with high energy consumption in the network,make full use of idle nodes with low energy consumption,and optimize the energy consumption dis-tribution of the whole network according to the continuous itera-tions of each traffic service flow.Simulation results show that,compared with the traditional shortest path algorithm,the pro-posed method can improve the balancing performance of nodes by 75%under certain traffic pressure,and realize the optimiza-tion of energy consumption balancing of the whole network.
文摘针对低轨卫星星地通信高动态信道特点,采用正交时频空(Orthogonal Time Frequency Space, OTFS)调制方式,提出一种低导频开销、高精度的两阶段信道估计方法,实现对时延、多普勒频移和信道增益3个参数的精细估计。所提TP-CSIE(Two Phase Channel State Information Estimation)方案采用时域训练序列为导频结构,解决时延-多普勒(Delay-Doppler, DD)域嵌入式导频方案在高动态星地链路下导频开销过大的问题。由于DD域信道的固有稀疏性,OTFS信道估计问题被转化为稀疏信号的恢复问题。在算法第一阶段,选用稀疏信号恢复算法进行信道参数的初始估计,利用重叠相加法获得部分先验信息以提高压缩采样匹配追踪(Compressive Sampling Matching Pursuit, CoSAMP)算法的准确性。在算法第二阶段,设计增强型旋转不变子空间算法实现信道参数的准确估计。仿真结果表明,与现有方案相比,所提算法归一化均方误差性能约有7 dB性能的提升,误码率性能约有10 dB的提升。
文摘针对低轨卫星通信场景下长传播时延导致的信道状态信息(Channel State Information,CSI)过时问题,提出了一种基于地理环境认知的低轨卫星空时多维信道预测方法。首先,通过射线追踪法建模卫星信道,进而确定不同地理环境下影响CSI变化的几个关键因素并将其与信道特征参量建立映射关系;然后,设计了一个由卷积神经网络(Convolutional Neural Network,CNN)和长短时记忆(Long⁃Short Term Memory,LSTM)神经网络构成的组合神经网络模型来有效预测CSI,通过CNN网络提取CSI和特征参量之间变化的空时相关性来认知地理环境对CSI变化的影响,利用LSTM网络处理时间序列的特点根据当前输入信息预测未来某一时刻CSI值。在此基础上,进一步提出了一种离线训练-模型更新-在线预测的实施框架以解决低轨卫星平台资源受限及高动态的问题。仿真结果表明,相较于传统的基于LSTM网络的低轨卫星信道预测方法,所提方法能够有效提升CSI预测精度及其预测模型的稳定性。
