With the deployment of ultra-dense low earth orbit(LEO)satellite constellations,LEO satellite access network(LEO-SAN)is envisioned to achieve global Internet coverage.Meanwhile,the civil aviation communications have i...With the deployment of ultra-dense low earth orbit(LEO)satellite constellations,LEO satellite access network(LEO-SAN)is envisioned to achieve global Internet coverage.Meanwhile,the civil aviation communications have increased dramatically,especially for providing airborne Internet services.However,due to dynamic service demands and onboard LEO resources over time and space,it poses huge challenges in satellite-aircraft access and service management in ultra-dense LEO satellite networks(UDLSN).In this paper,we propose a deep reinforcement learning-based approach for ultra-dense LEO satellite-aircraft access and service management.Firstly,we develop an airborne Internet architecture based on UDLSN and design a management mechanism including medium earth orbit satellites to guarantee lightweight management.Secondly,considering latency-sensitive and latency-tolerant services,we formulate the problem of satellite-aircraft access and service management for civil aviation to ensure service continuity.Finally,we propose a proximal policy optimization-based access and service management algorithm to solve the formulated problem.Simulation results demonstrate the convergence and effectiveness of the proposed algorithm with satisfying the service continuity when applying to the UDLSN.展开更多
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
Satellites in LEO (Low Earth Orbits) are closest to the Earth’s surface, having the smallest coverage area compared to other orbits, depending on altitude and elevation angle, and providing relatively too short visib...Satellites in LEO (Low Earth Orbits) are closest to the Earth’s surface, having the smallest coverage area compared to other orbits, depending on altitude and elevation angle, and providing relatively too short visibility and communication duration, in range of (2 - 15) minutes. Communication duration represents the key performance indicator for LEO satellite communication systems. For longer communication sessions, more satellites must be involved, and the signals must be handed over from one satellite to the next to provide uninterrupted real-time services to the appropriate user or ground station. This leads to the concept and structure of the satellites organized in the constellation. Communication window (visibility window) depends on the designed horizon plane width determined by licensed elevation angle. For the appropriate calculations, a satellite from the Starlink constellation at altitude of 550 km is considered, observed under licensed designed elevations of 40˚ and 25˚. Calculations under two designed elevation levels confirmed the wider horizon and consequently longer communication under the lower elevation.展开更多
Recently,a new worldwide race has emerged to achieve a breakthrough in designing and deploying massive ultra-dense low-Earth orbit(LEO)satellite constellation(SatCon)networks with the vision of providing everywhere In...Recently,a new worldwide race has emerged to achieve a breakthrough in designing and deploying massive ultra-dense low-Earth orbit(LEO)satellite constellation(SatCon)networks with the vision of providing everywhere Internet coverage from space.Several players have started the deployment phase with different scales.However,the implementation is in its infancy,and many investigations are needed.This work provides an overview of the stateof-the-art architectures,orbital patterns,top players,and potential applications of SatCon networks.Moreover,we discuss new open research directions and challenges for improving network performance.Finally,a case study highlights the benefits of integrating SatCon network and non-orthogonal multiple access(NOMA)technologies for improving the achievable capacity of satellite end-users.展开更多
自1980年Leo Fender与George Fullerton正式创立G&L以来,该品牌始终与高品质乐器相关联——但主要生存在Leo Fender首个同名公司、巨头Fender的阴影之下。数十年来,G&L推出了ASAT、S-500、Skyhawk、Nighthawk、Climax、Legacy以...自1980年Leo Fender与George Fullerton正式创立G&L以来,该品牌始终与高品质乐器相关联——但主要生存在Leo Fender首个同名公司、巨头Fender的阴影之下。数十年来,G&L推出了ASAT、S-500、Skyhawk、Nighthawk、Climax、Legacy以及Jerry Cantrell钟爱的Rampage等一系列型号——在Alice in Chains乐队“Man in the Box”音乐视频中,这位垃圾摇滚吉他手挥舞的正是此款吉他。展开更多
The existing Low-Earth-Orbit(LEO)positioning performance cannot meet the requirements of Unmanned Aerial Vehicle(UAV)clusters for high-precision real-time positioning in the Global Navigation Satellite System(GNSS)den...The existing Low-Earth-Orbit(LEO)positioning performance cannot meet the requirements of Unmanned Aerial Vehicle(UAV)clusters for high-precision real-time positioning in the Global Navigation Satellite System(GNSS)denial conditions.Therefore,this paper proposes a UAV Clusters Information Geometry Fusion Positioning(UC-IGFP)method using pseudoranges from the LEO satellites.A novel graph model for linking and computing between the UAV clusters and LEO satellites was established.By utilizing probability to describe the positional states of UAVs and sensor errors,the distributed multivariate Probability Fusion Cooperative Positioning(PF-CP)algorithm is proposed to achieve high-precision cooperative positioning and integration of the cluster.Criteria to select the centroid of the cluster were set.A new Kalman filter algorithm that is suitable for UAV clusters was designed based on the global benchmark and Riemann information geometry theory,which overcomes the discontinuity problem caused by the change of cluster centroids.Finally,the UC-IGFP method achieves the LEO continuous highprecision positioning of UAV clusters.The proposed method effectively addresses the positioning challenges caused by the strong direction of signal beams from LEO satellites and the insufficient constraint quantity of information sources at the edge nodes of the cluster.It significantly improves the accuracy and reliability of LEO-UAV cluster positioning.The results of comprehensive simulation experiments show that the proposed method has a 30.5%improvement in performance over the mainstream positioning methods,with a positioning error of 14.267 m.展开更多
As the new generation of low Earth orbit(LEO)satellite communication systems begins to provide high-speed broadband access services to areas without terrestrial cellular coverage,scholars both domestically and interna...As the new generation of low Earth orbit(LEO)satellite communication systems begins to provide high-speed broadband access services to areas without terrestrial cellular coverage,scholars both domestically and internationally are reassessing the relationship between satellite and ground communications in regions prone to warfare and sparsely populated areas.Especially after the launch of Starlink’s“Direct to Cell”service,many believe that new-generation LEO satellite communication systems may not just be a supplement to terrestrial networks in the future.Presently,the discourse surrounding satellite-terrestrial network technology predominantly centers on economic costs and user acceptance,with a noticeable gap in research that addresses green communication and sustainable development.This paper,therefore,aims to fill this void by modeling the energy consumption of LEO satellite communication systems,exemplified by Starlink,and juxtaposing it with that of terrestrial networks.Our findings indicate that the energy consumption of satellite communication systems,such as Starlink,is a staggering 32.9 times higher than that of ground base station clusters in remote regions and an astonishing 715 times greater in densely populated urban areas.Although satellite communication systems hold the promise of global coverage,their standalone construction without integration with terrestrial networks could lead to significant energy waste.展开更多
The rapid development of mega low earth orbit(LEO)satellite networks is expected to have a significant impact on 6G networks.Unlike terrestrial networks,due to the high-speed movement of satellites,users will frequent...The rapid development of mega low earth orbit(LEO)satellite networks is expected to have a significant impact on 6G networks.Unlike terrestrial networks,due to the high-speed movement of satellites,users will frequently hand over between satellites even if their positions remain unchanged.Furthermore,the extensive coverage characteristic of satellites leads to massive users executing handovers simultaneously.To address these challenges,we propose a novel double grouping-based group handover scheme(DGGH)specifically tailored for mega LEO satellite networks.First,we develop a user grouping strategy based on beam-limited hierarchical clustering to divide users into distinct groups.Next,we reframe the challenge of managing multiple users’simultaneous handovers as a single-objective optimization problem,solving it with a satellite grouping strategy that leverages the accuracy of greedy algorithms and the simplicity of dynamic programming.Additionally,we develop a group handover algorithm based on minimal handover waiting time to improve the satellite grouping process further.The detailed steps of the DGGH scheme’s handover procedure are meticulously outlined.Comprehensive simulations show that the proposed DGGH scheme outperforms single-user handover schemes in terms of handover signaling over-head and handover success rate.展开更多
Low Earth orbit(LEO)satellite networks have the advantages of low transmission delay and low deployment cost,playing an important role in providing reliable services to ground users.This paper studies an efficient int...Low Earth orbit(LEO)satellite networks have the advantages of low transmission delay and low deployment cost,playing an important role in providing reliable services to ground users.This paper studies an efficient inter-satellite cooperative computation offloading(ICCO)algorithm for LEO satellite networks.Specifically,an ICCO system model is constructed,which considers using neighboring satellites in the LEO satellite networks to collaboratively process tasks generated by ground user terminals,effectively improving resource utilization efficiency.Additionally,the optimization objective of minimizing the system task computation offloading delay and energy consumption is established,which is decoupled into two sub-problems.In terms of computational resource allocation,the convexity of the problem is proved through theoretical derivation,and the Lagrange multiplier method is used to obtain the optimal solution of computational resources.To deal with the task offloading decision,a dynamic sticky binary particle swarm optimization algorithm is designed to obtain the offloading decision by iteration.Simulation results show that the ICCO algorithm can effectively reduce the delay and energy consumption.展开更多
Low Earth Orbit(LEO)mega-constellation networks,exemplified by Starlink,are poised to play a pivotal role in future mobile communication networks,due to their low latency and high capacity.With the massively deployed ...Low Earth Orbit(LEO)mega-constellation networks,exemplified by Starlink,are poised to play a pivotal role in future mobile communication networks,due to their low latency and high capacity.With the massively deployed satellites,ground users now can be covered by multiple visible satellites,but also face complex handover issues with such massive high-mobility satellites in multi-layer.The end-to-end routing is also affected by the handover behavior.In this paper,we propose an intelligent handover strategy dedicated to multi-layer LEO mega-constellation networks.Firstly,an analytic model is utilized to rapidly estimate the end-to-end propagation latency as a key handover factor to construct a multi-objective optimization model.Subsequently,an intelligent handover strategy is proposed by employing the Dueling Double Deep Q Network(D3QN)-based deep reinforcement learning algorithm for single-layer constellations.Moreover,an optimal crosslayer handover scheme is proposed by predicting the latency-jitter and minimizing the cross-layer overhead.Simulation results demonstrate the superior performance of the proposed method in the multi-layer LEO mega-constellation,showcasing reductions of up to 8.2%and 59.5%in end-to-end latency and jitter respectively,when compared to the existing handover strategies.展开更多
The low Earth orbit(LEO)satellite networks have outstanding advantages such as wide coverage area and not being limited by geographic environment,which can provide a broader range of communication services and has bec...The low Earth orbit(LEO)satellite networks have outstanding advantages such as wide coverage area and not being limited by geographic environment,which can provide a broader range of communication services and has become an essential supplement to the terrestrial network.However,the dynamic changes and uneven distribution of satellite network traffic inevitably bring challenges to multipath routing.Even worse,the harsh space environment often leads to incomplete collection of network state data for routing decision-making,which further complicates this challenge.To address this problem,this paper proposes a state-incomplete intelligent dynamic multipath routing algorithm(SIDMRA)to maximize network efficiency even with incomplete state data as input.Specifically,we model the multipath routing problem as a markov decision process(MDP)and then combine the deep deterministic policy gradient(DDPG)and the K shortest paths(KSP)algorithm to solve the optimal multipath routing policy.We use the temporal correlation of the satellite network state to fit the incomplete state data and then use the message passing neuron network(MPNN)for data enhancement.Simulation results show that the proposed algorithm outperforms baseline algorithms regarding average end-to-end delay and packet loss rate and performs stably under certain missing rates of state data.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
基金supported in part by the National Key R&D Program of China under Grant 2020YFB1806104in part by Innovation and Entrepreneurship of Jiangsu Province High-level Talent Program+1 种基金in part by Natural Sciences and Engineering Research Council of Canada (NSERC)the support from Huawei
文摘With the deployment of ultra-dense low earth orbit(LEO)satellite constellations,LEO satellite access network(LEO-SAN)is envisioned to achieve global Internet coverage.Meanwhile,the civil aviation communications have increased dramatically,especially for providing airborne Internet services.However,due to dynamic service demands and onboard LEO resources over time and space,it poses huge challenges in satellite-aircraft access and service management in ultra-dense LEO satellite networks(UDLSN).In this paper,we propose a deep reinforcement learning-based approach for ultra-dense LEO satellite-aircraft access and service management.Firstly,we develop an airborne Internet architecture based on UDLSN and design a management mechanism including medium earth orbit satellites to guarantee lightweight management.Secondly,considering latency-sensitive and latency-tolerant services,we formulate the problem of satellite-aircraft access and service management for civil aviation to ensure service continuity.Finally,we propose a proximal policy optimization-based access and service management algorithm to solve the formulated problem.Simulation results demonstrate the convergence and effectiveness of the proposed algorithm with satisfying the service continuity when applying to the UDLSN.
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.
文摘Satellites in LEO (Low Earth Orbits) are closest to the Earth’s surface, having the smallest coverage area compared to other orbits, depending on altitude and elevation angle, and providing relatively too short visibility and communication duration, in range of (2 - 15) minutes. Communication duration represents the key performance indicator for LEO satellite communication systems. For longer communication sessions, more satellites must be involved, and the signals must be handed over from one satellite to the next to provide uninterrupted real-time services to the appropriate user or ground station. This leads to the concept and structure of the satellites organized in the constellation. Communication window (visibility window) depends on the designed horizon plane width determined by licensed elevation angle. For the appropriate calculations, a satellite from the Starlink constellation at altitude of 550 km is considered, observed under licensed designed elevations of 40˚ and 25˚. Calculations under two designed elevation levels confirmed the wider horizon and consequently longer communication under the lower elevation.
基金Ehab Mahmoud Mohamed is supported via funding from Prince sattam bin Abdulaziz University project number(PSAU/2025/R/1446).
文摘Recently,a new worldwide race has emerged to achieve a breakthrough in designing and deploying massive ultra-dense low-Earth orbit(LEO)satellite constellation(SatCon)networks with the vision of providing everywhere Internet coverage from space.Several players have started the deployment phase with different scales.However,the implementation is in its infancy,and many investigations are needed.This work provides an overview of the stateof-the-art architectures,orbital patterns,top players,and potential applications of SatCon networks.Moreover,we discuss new open research directions and challenges for improving network performance.Finally,a case study highlights the benefits of integrating SatCon network and non-orthogonal multiple access(NOMA)technologies for improving the achievable capacity of satellite end-users.
文摘自1980年Leo Fender与George Fullerton正式创立G&L以来,该品牌始终与高品质乐器相关联——但主要生存在Leo Fender首个同名公司、巨头Fender的阴影之下。数十年来,G&L推出了ASAT、S-500、Skyhawk、Nighthawk、Climax、Legacy以及Jerry Cantrell钟爱的Rampage等一系列型号——在Alice in Chains乐队“Man in the Box”音乐视频中,这位垃圾摇滚吉他手挥舞的正是此款吉他。
基金supported in part by the National Natural Science Foundation of China(Nos.62171375,62271397,62001392,62101458,62173276,61803310 and 61801394)the Shenzhen Science and Technology Innovation ProgramChina(No.JCYJ20220530161615033)。
文摘The existing Low-Earth-Orbit(LEO)positioning performance cannot meet the requirements of Unmanned Aerial Vehicle(UAV)clusters for high-precision real-time positioning in the Global Navigation Satellite System(GNSS)denial conditions.Therefore,this paper proposes a UAV Clusters Information Geometry Fusion Positioning(UC-IGFP)method using pseudoranges from the LEO satellites.A novel graph model for linking and computing between the UAV clusters and LEO satellites was established.By utilizing probability to describe the positional states of UAVs and sensor errors,the distributed multivariate Probability Fusion Cooperative Positioning(PF-CP)algorithm is proposed to achieve high-precision cooperative positioning and integration of the cluster.Criteria to select the centroid of the cluster were set.A new Kalman filter algorithm that is suitable for UAV clusters was designed based on the global benchmark and Riemann information geometry theory,which overcomes the discontinuity problem caused by the change of cluster centroids.Finally,the UC-IGFP method achieves the LEO continuous highprecision positioning of UAV clusters.The proposed method effectively addresses the positioning challenges caused by the strong direction of signal beams from LEO satellites and the insufficient constraint quantity of information sources at the edge nodes of the cluster.It significantly improves the accuracy and reliability of LEO-UAV cluster positioning.The results of comprehensive simulation experiments show that the proposed method has a 30.5%improvement in performance over the mainstream positioning methods,with a positioning error of 14.267 m.
基金the National Key R&D Program of China“6G satellite communication access networking technology”(No.2020YFB1808000).
文摘As the new generation of low Earth orbit(LEO)satellite communication systems begins to provide high-speed broadband access services to areas without terrestrial cellular coverage,scholars both domestically and internationally are reassessing the relationship between satellite and ground communications in regions prone to warfare and sparsely populated areas.Especially after the launch of Starlink’s“Direct to Cell”service,many believe that new-generation LEO satellite communication systems may not just be a supplement to terrestrial networks in the future.Presently,the discourse surrounding satellite-terrestrial network technology predominantly centers on economic costs and user acceptance,with a noticeable gap in research that addresses green communication and sustainable development.This paper,therefore,aims to fill this void by modeling the energy consumption of LEO satellite communication systems,exemplified by Starlink,and juxtaposing it with that of terrestrial networks.Our findings indicate that the energy consumption of satellite communication systems,such as Starlink,is a staggering 32.9 times higher than that of ground base station clusters in remote regions and an astonishing 715 times greater in densely populated urban areas.Although satellite communication systems hold the promise of global coverage,their standalone construction without integration with terrestrial networks could lead to significant energy waste.
基金supported in part by the State Key Laboratory of Micro-Spacecraft Rapid Design and Intelligent Cluster(No.MS01240103)the National Natural Science Foundation of China(No.62071146,No.62431009)+1 种基金the National 2011 Collaborative Innovation Center of Wireless Communication Technologies(No.2242022k60006)the Research Project Fund of Songjiang Laboratory(No.SL20230104).
文摘The rapid development of mega low earth orbit(LEO)satellite networks is expected to have a significant impact on 6G networks.Unlike terrestrial networks,due to the high-speed movement of satellites,users will frequently hand over between satellites even if their positions remain unchanged.Furthermore,the extensive coverage characteristic of satellites leads to massive users executing handovers simultaneously.To address these challenges,we propose a novel double grouping-based group handover scheme(DGGH)specifically tailored for mega LEO satellite networks.First,we develop a user grouping strategy based on beam-limited hierarchical clustering to divide users into distinct groups.Next,we reframe the challenge of managing multiple users’simultaneous handovers as a single-objective optimization problem,solving it with a satellite grouping strategy that leverages the accuracy of greedy algorithms and the simplicity of dynamic programming.Additionally,we develop a group handover algorithm based on minimal handover waiting time to improve the satellite grouping process further.The detailed steps of the DGGH scheme’s handover procedure are meticulously outlined.Comprehensive simulations show that the proposed DGGH scheme outperforms single-user handover schemes in terms of handover signaling over-head and handover success rate.
基金supported in part by Sub Project of National Key Research and Development plan in 2020 NO.2020YFC1511704Beijing Information Science and Technology University NO.2020KYNH212,NO.2021CGZH302+1 种基金Beijing Science and Technology Project(Grant No.Z211100004421009)in part by the National Natural Science Foundation of China(Grant No.62301058).
文摘Low Earth orbit(LEO)satellite networks have the advantages of low transmission delay and low deployment cost,playing an important role in providing reliable services to ground users.This paper studies an efficient inter-satellite cooperative computation offloading(ICCO)algorithm for LEO satellite networks.Specifically,an ICCO system model is constructed,which considers using neighboring satellites in the LEO satellite networks to collaboratively process tasks generated by ground user terminals,effectively improving resource utilization efficiency.Additionally,the optimization objective of minimizing the system task computation offloading delay and energy consumption is established,which is decoupled into two sub-problems.In terms of computational resource allocation,the convexity of the problem is proved through theoretical derivation,and the Lagrange multiplier method is used to obtain the optimal solution of computational resources.To deal with the task offloading decision,a dynamic sticky binary particle swarm optimization algorithm is designed to obtain the offloading decision by iteration.Simulation results show that the ICCO algorithm can effectively reduce the delay and energy consumption.
基金supported by the National Natural Science Foundation of China(No.62401597)Natural Science Foundation of Hunan Province,China(No.2024JJ6469)the Research Project of National University of Defense Technology,China(No.ZK22-02).
文摘Low Earth Orbit(LEO)mega-constellation networks,exemplified by Starlink,are poised to play a pivotal role in future mobile communication networks,due to their low latency and high capacity.With the massively deployed satellites,ground users now can be covered by multiple visible satellites,but also face complex handover issues with such massive high-mobility satellites in multi-layer.The end-to-end routing is also affected by the handover behavior.In this paper,we propose an intelligent handover strategy dedicated to multi-layer LEO mega-constellation networks.Firstly,an analytic model is utilized to rapidly estimate the end-to-end propagation latency as a key handover factor to construct a multi-objective optimization model.Subsequently,an intelligent handover strategy is proposed by employing the Dueling Double Deep Q Network(D3QN)-based deep reinforcement learning algorithm for single-layer constellations.Moreover,an optimal crosslayer handover scheme is proposed by predicting the latency-jitter and minimizing the cross-layer overhead.Simulation results demonstrate the superior performance of the proposed method in the multi-layer LEO mega-constellation,showcasing reductions of up to 8.2%and 59.5%in end-to-end latency and jitter respectively,when compared to the existing handover strategies.
文摘The low Earth orbit(LEO)satellite networks have outstanding advantages such as wide coverage area and not being limited by geographic environment,which can provide a broader range of communication services and has become an essential supplement to the terrestrial network.However,the dynamic changes and uneven distribution of satellite network traffic inevitably bring challenges to multipath routing.Even worse,the harsh space environment often leads to incomplete collection of network state data for routing decision-making,which further complicates this challenge.To address this problem,this paper proposes a state-incomplete intelligent dynamic multipath routing algorithm(SIDMRA)to maximize network efficiency even with incomplete state data as input.Specifically,we model the multipath routing problem as a markov decision process(MDP)and then combine the deep deterministic policy gradient(DDPG)and the K shortest paths(KSP)algorithm to solve the optimal multipath routing policy.We use the temporal correlation of the satellite network state to fit the incomplete state data and then use the message passing neuron network(MPNN)for data enhancement.Simulation results show that the proposed algorithm outperforms baseline algorithms regarding average end-to-end delay and packet loss rate and performs stably under certain missing rates of state data.
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.
