With the large-scale deployment of satellite constellations such as Starlink and the rapid advancement of technologies including artificial intelligence (AI) and non-terrestrial networks (NTNs), the integration of hig...With the large-scale deployment of satellite constellations such as Starlink and the rapid advancement of technologies including artificial intelligence (AI) and non-terrestrial networks (NTNs), the integration of high, medium, and low Earth orbit satellite networks with terrestrial networks has become a critical direction for future communication technologies. The objective is to develop a space-terrestrial integrated 6G network that ensures ubiquitous connectivity and seamless services, facilitating intelligent interconnection and collaborative symbiosis among humans, machines, and objects. This integration has become a central focus of global technological innovation.展开更多
Next-GenerationNetworks(NGNs)demand high resilience,dynamic adaptability,and efficient resource utilization to enable ubiquitous connectivity.In this context,the Space-Air-Ground Integrated Network(SAGIN)architecture ...Next-GenerationNetworks(NGNs)demand high resilience,dynamic adaptability,and efficient resource utilization to enable ubiquitous connectivity.In this context,the Space-Air-Ground Integrated Network(SAGIN)architecture is uniquely positioned to meet these requirements.However,conventional NGN routing algorithms often fail to account for SAGIN’s intrinsic characteristics,such as its heterogeneous structure,dynamic topology,and constrained resources,leading to suboptimal performance under disruptions such as node failures or cyberattacks.To meet these demands for SAGIN,this study proposes a resilience-oriented routing optimization framework featuring dynamic weighting and multi-objective evaluation.Methodologically,we define three core routing performance metrics,quantified through a four-dimensionalmodel,encompassing robustness Rd,resilience Rr,adaptability Ra,and resource utilization efficiency Ru,and integrate them into a comprehensive evaluation metric.In simulated SAGIN environments,the proposed Multi-Indicator Weighted Resilience Evaluation Algorithm(MIW-REA)demonstrates significant improvements in resilience enhancement,recovery acceleration,and resource optimization.It maintains 82.3%service availability even with a 30%node failure rate,reduces Distributed Denial of Service(DDoS)attack recovery time by 43%,decreases bandwidth waste by 23.4%,and lowers energy consumption by 18.9%.By addressing challenges unique to the SAGIN network,this research provides a flexible real-time solution for NGN routing optimization that balances resilience,efficiency,and adaptability,advancing the field.展开更多
This paper investigates the traffic offloading optimization challenge in Space-Air-Ground Integrated Networks(SAGIN)through a novel Recursive Multi-Agent Proximal Policy Optimization(RMAPPO)algorithm.The exponential g...This paper investigates the traffic offloading optimization challenge in Space-Air-Ground Integrated Networks(SAGIN)through a novel Recursive Multi-Agent Proximal Policy Optimization(RMAPPO)algorithm.The exponential growth of mobile devices and data traffic has substantially increased network congestion,particularly in urban areas and regions with limited terrestrial infrastructure.Our approach jointly optimizes unmanned aerial vehicle(UAV)trajectories and satellite-assisted offloading strategies to simultaneously maximize data throughput,minimize energy consumption,and maintain equitable resource distribution.The proposed RMAPPO framework incorporates recurrent neural networks(RNNs)to model temporal dependencies in UAV mobility patterns and utilizes a decentralized multi-agent reinforcement learning architecture to reduce communication overhead while improving system robustness.The proposed RMAPPO algorithm was evaluated through simulation experiments,with the results indicating that it significantly enhances the cumulative traffic offloading rate of nodes and reduces the energy consumption of UAVs.展开更多
An efficient data-driven numerical framework is developed for transient heat conduction analysis in thin-walled structures.The proposed approach integrates spectral time discretization with neural network approximatio...An efficient data-driven numerical framework is developed for transient heat conduction analysis in thin-walled structures.The proposed approach integrates spectral time discretization with neural network approximation,forming a spectral-integrated neural network(SINN)scheme tailored for problems characterized by long-time evolution.Temporal derivatives are treated through a spectral integration strategy based on orthogonal polynomial expansions,which significantly alleviates stability constraints associated with conventional time-marching schemes.A fully connected neural network is employed to approximate the temperature-related variables,while governing equa-tions and boundary conditions are enforced through a physics-informed loss formulation.Numerical investigations demonstrate that the proposed method maintains high accuracy even when large time steps are adopted,where standard numerical solvers often suffer from instability or excessive computational cost.Moreover,the framework exhibits strong robustness for ultrathin configurations with extreme aspect ratios,achieving relative errors on the order of 10−5 or lower.These results indicate that the SINN framework provides a reliable and efficient alternative for transient thermal analysis of thin-walled structures under challenging computational conditions.展开更多
The future 6G networks will integrates space and terrestrial networks to realize a fully connected world with extensive collaboration.However,how to build trust between multiple parties is a difficult problem for secu...The future 6G networks will integrates space and terrestrial networks to realize a fully connected world with extensive collaboration.However,how to build trust between multiple parties is a difficult problem for secure cooperation without a reliable third-party.Blockchain is a promising technology to solve this problem by converting the trust between multi-parties to the trust to the common shared data.Several works have proposed to apply the incentive mechanism in blockchain to encourage effective cooperation,but how to evaluate the cooperation performance and avoid breach of contract is not discussed.In this paper,a secure relay scheme is proposed based on the consortium blockchain system composed by different operators.In particular,smart contract checks the integrity of the message based on RSA accumulator,and executes transactions automatically when the message is delivered successfully.Detailed procedures are introduced for both uplink and downlink relay.Implementation based on Hyperledger Fabric proves the effectiveness of the proposed scheme and shows that the complexity of the scheme is low enough for practical deployment.展开更多
With the rapid development of low-orbit satellite com-munication networks both domestically and internationally,space-terrestrial integrated networks will become the future development trend.For space and terrestrial ...With the rapid development of low-orbit satellite com-munication networks both domestically and internationally,space-terrestrial integrated networks will become the future development trend.For space and terrestrial networks with limi-ted resources,the utilization efficiency of the entire space-terres-trial integrated networks resources can be affected by the core network indirectly.In order to improve the response efficiency of core networks expansion construction,early warning of the core network elements capacity is necessary.Based on the inte-grated architecture of space and terrestrial network,multidimen-sional factors are considered in this paper,including the number of terminals,login users,and the rules of users’migration during holidays.Using artifical intelligence(AI)technologies,the regis-tered users of the access and mobility management function(AMF),authorization users of the unified data management(UDM),protocol data unit(PDU)sessions of session manage-ment function(SMF)are predicted in combination with the num-ber of login users,the number of terminals.Therefore,the core network elements capacity can be predicted in advance.The proposed method is proven to be effective based on the data from real network.展开更多
In December 2025,the ASEAN Centre for Energy(ACE)convened the third ASEAN Power Grid Partnership Meeting,bringing partners together for consultations on key issues.After more than two decades of planning and explorati...In December 2025,the ASEAN Centre for Energy(ACE)convened the third ASEAN Power Grid Partnership Meeting,bringing partners together for consultations on key issues.After more than two decades of planning and exploration,the ASEAN Power Grid is now entering a new phase—shifting from predominantly bilateral,one-way connections toward a multilateral,multidirectional network.展开更多
Photonic neural networks(PNNs)of sufficiently large physical dimensions and high operation accuracies are envisaged as ideal candidates for breaking the major bottlenecks in the current artificial intelligence archite...Photonic neural networks(PNNs)of sufficiently large physical dimensions and high operation accuracies are envisaged as ideal candidates for breaking the major bottlenecks in the current artificial intelligence architectures in terms of latency,energy efficiency,and computational power.To achieve this vision,it is of vital importance to scale up the PNNs while simultaneously reducing the high demand on the dimensions required by them.The underlying cause of this strategy is the enormous gap between the scales of photonic and electronic integrated circuits.Here,we demonstrate monolithically integrated optical convolutional processors on thin film lithium niobate(TFLN)that harness inherent parallelism in photonics to enable large-scale programmable convolution kernels and,in turn,greatly reduce the dimensions required by subsequent fully connected layers.Experimental validation achieves high classification accuracies of 96%(86%)on the MNIST(Fashion-MNIST)dataset and 84.6%on the AG News dataset while dramatically reducing the required subsequent fully connected layer dimensions to 196×10(from 784×10)and 175×4(from 800×4),respectively.Furthermore,our devices can be driven by commercial field-programmable gate array systems;a unique advantage in addition to their scalable channel number and kernel size.Our architecture provides a solution to build practical machine learning photonic devices.展开更多
The rapid expansion of the low-altitude economy is driving strong demand for highly accurate and reliable positioning technologies to support diverse aerial operations.This review examines core positioning methodologi...The rapid expansion of the low-altitude economy is driving strong demand for highly accurate and reliable positioning technologies to support diverse aerial operations.This review examines core positioning methodologies within the low-altitude intelligent network(LAIN)framework,beginning with an analysis of positioning requirements and performance metrics for low-altitude flight scenarios.It systematically assesses the principles,strengths,and limitations of mainstream positioning systems,including Global Navigation Satellite Systems(GNSS),terrestrial wireless positioning,and autonomous navigation,and it surveys prevalent integrated and cooperative positioning schemes.Our analysis demonstrates that standalone positioning technologies are inadequate in complex low-altitude settings,underscoring the pivotal role of multi-source fusion and unmanned aerial vehicle(UAV)swarm cooperative positioning as future trends.To address infrastructure gaps and high deployment costs in current LAIN systems,we propose a“space−air−ground”integrated and cooperative positioning architecture centered on GNSS and the 5th generation mobile communication technology(5G).The ground layer integrates 5G and GNSS for wide-area enhanced positioning.The aerial layer uses 5G aircraft-to-everything(A2X)and sidelink(SL)communications to build self-organizing networks for cooperative UAV localization.The space layer leverages low Earth orbit(LEO)satellites to overcome coverage limitations in communication and positioning.This hierarchical architecture reduces deployment costs through infrastructure reuse and enables deep integration of communication and navigation capabilities.By supporting collaborative enhancement across all three domains,the framework improves positioning robustness and delivers cost-effective,ubiquitous,and highly reliable positioning services.Finally,we outline promising research directions.This review aims to provide a systematic reference and a novel architectural perspective for the ongoing development of LAIN.展开更多
Accurate estimation of mineralogy from geophysical well logs is crucial for characterizing geological formations,particularly in hydrocarbon exploration,CO_(2) sequestration,and geothermal energy development.Current t...Accurate estimation of mineralogy from geophysical well logs is crucial for characterizing geological formations,particularly in hydrocarbon exploration,CO_(2) sequestration,and geothermal energy development.Current techniques,such as multimineral petrophysical analysis,offer details into mineralogical distribution.However,it is inherently time-intensive and demands substantial geological expertise for accurate model evaluation.Furthermore,traditional machine learning techniques often struggle to predict mineralogy accurately and sometimes produce estimations that violate fundamental physical principles.To address this,we present a new approach using Physics-Integrated Neural Networks(PINNs),that combines data-driven learning with domain-specific physical constraints,embedding petrophysical relationships directly into the neural network architecture.This approach enforces that predictions adhere to physical laws.The methodology is applied to the Broom Creek Deep Saline aquifer,a CO_(2) sequestration site in the Williston Basin,to predict the volumes of key mineral constituents—quartz,dolomite,feldspar,anhydrite,illite—along with porosity.Compared to traditional artificial neural networks (ANN),the PINN approach demonstrates higher accuracy and better generalizability,significantly enhancing predictive performance on unseen well datasets.The average mean error across the three blind wells is 0.123 for ANN and 0.042 for PINN,highlighting the superior accuracy of the PINN approach.This method reduces uncertainties in reservoir characterization by improving the reliability of mineralogy and porosity predictions,providing a more robust tool for decision-making in various subsurface geoscience applications.展开更多
Phytomelatonin,an emerging plant hormone,plays vital roles in plant growth,development,and stress adaptation(Arnao et al.,2022;Ullah et al.,2024).It acts both as a direct antioxidant and a signaling molecule,engaging ...Phytomelatonin,an emerging plant hormone,plays vital roles in plant growth,development,and stress adaptation(Arnao et al.,2022;Ullah et al.,2024).It acts both as a direct antioxidant and a signaling molecule,engaging complex networks and interacting with other phytohormones(Liu et al.,2022;Khan et al.,2023).Although phytomelatonin receptors(PMTRs)have been identified in many plants(Wei et al.,2018;Wang et al.,2022;Liu et al.,2025),the downstream signaling mechanisms,particularly receptor-mediated protein modifications and transcriptional regulation,remain poorly characterized.展开更多
Satellite-terrestrial networks have garnered significant attention in recent years and are extensively applied in intelligent transportation and emergency rescue.This paper provides a comprehensive review of the lates...Satellite-terrestrial networks have garnered significant attention in recent years and are extensively applied in intelligent transportation and emergency rescue.This paper provides a comprehensive review of the latest research advancements in satellite-terrestrial integrated network(STIN)technologies from a network perspective,dividing STIN technologies into three categories according to network service flows—namely,topology maintenance,network routing,and orchestration transmission technologies.Furthermore,a novel network-layer perspective is considered to examine the applications of STINs across various domains,along with related frameworks,platforms,simulators,and datasets.Finally,this paper explores the mainstream research directions in STIN technologies,with an innovative focus on the network layer.It reviews the existing literature,outlines future trends,and discusses opportunities for collaboration with related fields.展开更多
The sixth-generation(6G)networks will consist of multiple bands such as low-frequency,midfrequency,millimeter wave,terahertz and other bands to meet various business requirements and networking scenarios.The dynamic c...The sixth-generation(6G)networks will consist of multiple bands such as low-frequency,midfrequency,millimeter wave,terahertz and other bands to meet various business requirements and networking scenarios.The dynamic complementarity of multiple bands are crucial for enhancing the spectrum efficiency,reducing network energy consumption,and ensuring a consistent user experience.This paper investigates the present researches and challenges associated with deployment of multi-band integrated networks in existing infrastructures.Then,an evolutionary path for integrated networking is proposed with the consideration of maturity of emerging technologies and practical network deployment.The proposed design principles for 6G multi-band integrated networking aim to achieve on-demand networking objectives,while the architecture supports full spectrum access and collaboration between high and low frequencies.In addition,the potential key air interface technologies and intelligent technologies for integrated networking are comprehensively discussed.It will be a crucial basis for the subsequent standards promotion of 6G multi-band integrated networking technology.展开更多
Blockchain-based spectrum sharing with consensus is the key technology for sixth-generation mobile communication to realize dynamic spectrum management.In order to avoid the waste of computing and communication resour...Blockchain-based spectrum sharing with consensus is the key technology for sixth-generation mobile communication to realize dynamic spectrum management.In order to avoid the waste of computing and communication resources,a spectrum sharing policy-based consensus mechanism is proposed in this paper.Firstly,a spectrum sharing algorithm based on graph neural network is designed in the satelliteterrestrial spectrum sharing networks under the underlay model.It avoids high computational overhead of the traditional iterative optimization algorithm when the wireless channel condition and network topology are highly dynamic.Secondly,a consensus mechanism based on spectrum sharing strategy is designed,which converts the traditional meaningless hash problem into the graph neural network training.Miners compete for accounting rights by training a graph neutral network model that meets the spectrum sharing requirement.Finally,the consensus delay,communication and storage overhead of the proposed consensus mechanism are analyzed theoretically.The simulation results show that the proposed consensus mechanism can effectively improve spectrum efficiency with excellent scalability and generalization performance.展开更多
To support ubiquitous communication and enhance other 6G applications,the Space-Air-Ground Integrated Network(SAGIN)has become a research hotspot.Traditionally,satellite-ground fusion technologies integrate network en...To support ubiquitous communication and enhance other 6G applications,the Space-Air-Ground Integrated Network(SAGIN)has become a research hotspot.Traditionally,satellite-ground fusion technologies integrate network entities from space,aerial,and terrestrial domains.However,they face challenges such as spectrum scarcity and inefficient satellite handover.This paper explores the Channel-Aware Handover Management(CAHM)strategy in SAGIN for data allocation.Specifically,CAHM utilizes the data receiving capability of Low Earth Orbit(LEO)satellites,considering satellite-ground distance,free-space path loss,and channel gain.Furthermore,CAHM assesses LEO satellite data forwarding capability using signal-to-noise ratio,link duration and buffer queue length.Then,CAHM applies historical data on LEO satellite transmission successes and failures to effectively reduce overall interruption ratio.Simulation results show that CAHM outperforms baseline algorithms in terms of delivery ratio,latency,and interruption ratio.展开更多
In recent years,load balancing routing al-gorithms have been extensively studied in satellite net-works.Most existing studies focus on path selection and hop-count optimization for end-to-end transmis-sion,while overl...In recent years,load balancing routing al-gorithms have been extensively studied in satellite net-works.Most existing studies focus on path selection and hop-count optimization for end-to-end transmis-sion,while overlooking congestion issues on feeder links caused by the limited number and centralized distribution of ground stations.Hence,a multi-service routing algorithm called the Multi-service Load Bal-ancing Routing Algorithm for Traffic Return(MLB-TR)is proposed.Unlike traditional approaches,MLB-TR aims to achieve a broader and more comprehensive load balancing objective.Specifically,based on the service type,an appropriate landing satellite is first selected by considering factors such as shortest path hop count and satellite load.Then,a set of candidate paths from the source satellite to the selected landing satellite is computed.Finally,using the regional load balancing index as the optimization objective,the final transmission path is selected from the candidate path set.Simulation results show that the proposed algo-rithm outperforms the existing works.展开更多
Rising demands for bandwidth,speed,and energy efficiency are reshaping the landscape of computing beyond the limits of von Neumann electronics.Neuromorphic photonics—using light to emulate neural computation—offers ...Rising demands for bandwidth,speed,and energy efficiency are reshaping the landscape of computing beyond the limits of von Neumann electronics.Neuromorphic photonics—using light to emulate neural computation—offers ultrafast,massively parallel,and low-energy information processing,positioning integrated photonic neural networks(IPNNs)as promising hardware for next-generation artificial intelligence(AI).By combining the architectural efficiency of neuromorphic models with the physical advantages of integrated photonics,IPNNs enable high-speed and programmable linear operations during the in-plane optical transmission,while leaving room for compact and reconfigurable on-chip optical nonlinearities and memory functions.Firstly,we review the concepts and principles of key building blocks in IPNN,that are photonic synapses,neurons,and photonic memristors which offer optical memory and storage capabilities.And then,we summarize the representative IPNN architectures and their recent advances,including coherent,parallel,diffractive,and reservoir computing,for photonic neuromorphic computing with high throughput and high efficiency.Finally,we outline practical considerations—calibration and stability of large-scale networks,routes toward co-integration with electronics,diffractive–interferometric hybrid architectures,and programmable photonic architectures for general AI purposes.We highlight a forward outlook on enabling IPNN with low energy consumption,robust photonic operations,and efficient training strategies,aiming to guide the maturation of general-purpose,low-power photonic AI.展开更多
In this paper,we propose a joint power and frequency allocation algorithm considering interference protection in the integrated satellite and terrestrial network(ISTN).We efficiently utilize spectrum resources by allo...In this paper,we propose a joint power and frequency allocation algorithm considering interference protection in the integrated satellite and terrestrial network(ISTN).We efficiently utilize spectrum resources by allowing user equipment(UE)of terrestrial networks to share frequencies with satellite networks.In order to protect the satellite terminal(ST),the base station(BS)needs to control the transmit power and frequency resources of the UE.The optimization problem involves maximizing the achievable throughput while satisfying the interference protection constraints of the ST and the quality of service(QoS)of the UE.However,this problem is highly nonconvex,and we decompose it into power allocation and frequency resource scheduling subproblems.In the power allocation subproblem,we propose a power allocation algorithm based on interference probability(PAIP)to address channel uncertainty.We obtain the suboptimal power allocation solution through iterative optimization.In the frequency resource scheduling subproblem,we develop a heuristic algorithm to handle the non-convexity of the problem.The simulation results show that the combination of power allocation and frequency resource scheduling algorithms can improve spectrum utilization.展开更多
The rapid growth of low-Earth-orbit satellites has injected new vitality into future service provisioning.However,given the inherent volatility of network traffic,ensuring differentiated quality of service in highly d...The rapid growth of low-Earth-orbit satellites has injected new vitality into future service provisioning.However,given the inherent volatility of network traffic,ensuring differentiated quality of service in highly dynamic networks remains a significant challenge.In this paper,we propose an online learning-based resource scheduling scheme for satellite-terrestrial integrated networks(STINs)aimed at providing on-demand services with minimal resource utilization.Specifically,we focus on:①accurately characterizing the STIN channel,②predicting resource demand with uncertainty guarantees,and③implementing mixed timescale resource scheduling.For the STIN channel,we adopt the 3rd Generation Partnership Project channel and antenna models for non-terrestrial networks.We employ a one-dimensional convolution and attention-assisted long short-term memory architecture for average demand prediction,while introducing conformal prediction to mitigate uncertainties arising from burst traffic.Additionally,we develop a dual-timescale optimization framework that includes resource reservation on a larger timescale and resource adjustment on a smaller timescale.We also designed an online resource scheduling algorithm based on online convex optimization to guarantee long-term performance with limited knowledge of time-varying network information.Based on the Network Simulator 3 implementation of the STIN channel under our high-fidelity satellite Internet simulation platform,numerical results using a real-world dataset demonstrate the accuracy and efficiency of the prediction algorithms and online resource scheduling scheme.展开更多
The lack of communication infrastructure in remote regions presents significant obstacles to gathering data from smart power sensors(SPSs)in smart grid networks.In such cases,a space-air-ground integrated network serv...The lack of communication infrastructure in remote regions presents significant obstacles to gathering data from smart power sensors(SPSs)in smart grid networks.In such cases,a space-air-ground integrated network serves as an effective emergency solution.This study addresses the challenge of optimizing the energy efficiency of data transmission fromSPSs to low Earth orbit(LEO)satellites through unmanned aerial vehicles(UAVs),considering both effective capacity and fronthaul link capacity constraints.Due to the non-convex nature of the problem,the objective function is reformulated,and a delay-aware energy-efficient power allocation and UAV trajectory design(DEPATD)algorithm is proposed as a two-loop approach.Since the inner loop remains non-convex,the block coordinate descent(BCD)method is employed to decompose it into three subproblems:power allocation for SPSs,power allocation for UAVs,and UAV trajectory design.The first two subproblems are solved using the Lagrangian dual method,while the third is addressed with the successive convex approximation(SCA)technique.By iteratively solving these subproblems,an efficient algorithm is developed to resolve the inner loop issue.Simulation results demonstrate that the energy efficiency of the proposed DEPATD algorithm improves by 4.02% compared to the benchmark algorithm when the maximum transmission power of the SPSs increases from 0.1 to 0.45W.展开更多
文摘With the large-scale deployment of satellite constellations such as Starlink and the rapid advancement of technologies including artificial intelligence (AI) and non-terrestrial networks (NTNs), the integration of high, medium, and low Earth orbit satellite networks with terrestrial networks has become a critical direction for future communication technologies. The objective is to develop a space-terrestrial integrated 6G network that ensures ubiquitous connectivity and seamless services, facilitating intelligent interconnection and collaborative symbiosis among humans, machines, and objects. This integration has become a central focus of global technological innovation.
基金supported by the Beijing Natural Science Foundation under Grant 9242003partially supported by the Natural Science Foundation of Chongqing,China under Grant CSTB2023NSCQ-MSX0391+3 种基金partially supported by the National Natural Science Foundation of China under Grant 62471493partially supported by the Natural Science Foundation of Shandong Province under Grants ZR2023LZH017,ZR2024MF066supported by the Key Laboratory of Public Opinion Governance and Computational Communication under Grant YQKFYB202501The Research Project on the Development of Social Sciences in Hebei Province in 2024(No.202403150).
文摘Next-GenerationNetworks(NGNs)demand high resilience,dynamic adaptability,and efficient resource utilization to enable ubiquitous connectivity.In this context,the Space-Air-Ground Integrated Network(SAGIN)architecture is uniquely positioned to meet these requirements.However,conventional NGN routing algorithms often fail to account for SAGIN’s intrinsic characteristics,such as its heterogeneous structure,dynamic topology,and constrained resources,leading to suboptimal performance under disruptions such as node failures or cyberattacks.To meet these demands for SAGIN,this study proposes a resilience-oriented routing optimization framework featuring dynamic weighting and multi-objective evaluation.Methodologically,we define three core routing performance metrics,quantified through a four-dimensionalmodel,encompassing robustness Rd,resilience Rr,adaptability Ra,and resource utilization efficiency Ru,and integrate them into a comprehensive evaluation metric.In simulated SAGIN environments,the proposed Multi-Indicator Weighted Resilience Evaluation Algorithm(MIW-REA)demonstrates significant improvements in resilience enhancement,recovery acceleration,and resource optimization.It maintains 82.3%service availability even with a 30%node failure rate,reduces Distributed Denial of Service(DDoS)attack recovery time by 43%,decreases bandwidth waste by 23.4%,and lowers energy consumption by 18.9%.By addressing challenges unique to the SAGIN network,this research provides a flexible real-time solution for NGN routing optimization that balances resilience,efficiency,and adaptability,advancing the field.
文摘This paper investigates the traffic offloading optimization challenge in Space-Air-Ground Integrated Networks(SAGIN)through a novel Recursive Multi-Agent Proximal Policy Optimization(RMAPPO)algorithm.The exponential growth of mobile devices and data traffic has substantially increased network congestion,particularly in urban areas and regions with limited terrestrial infrastructure.Our approach jointly optimizes unmanned aerial vehicle(UAV)trajectories and satellite-assisted offloading strategies to simultaneously maximize data throughput,minimize energy consumption,and maintain equitable resource distribution.The proposed RMAPPO framework incorporates recurrent neural networks(RNNs)to model temporal dependencies in UAV mobility patterns and utilizes a decentralized multi-agent reinforcement learning architecture to reduce communication overhead while improving system robustness.The proposed RMAPPO algorithm was evaluated through simulation experiments,with the results indicating that it significantly enhances the cumulative traffic offloading rate of nodes and reduces the energy consumption of UAVs.
基金supported by the National Natural Science Foundation of China(Nos.12422207 and 12372199).
文摘An efficient data-driven numerical framework is developed for transient heat conduction analysis in thin-walled structures.The proposed approach integrates spectral time discretization with neural network approximation,forming a spectral-integrated neural network(SINN)scheme tailored for problems characterized by long-time evolution.Temporal derivatives are treated through a spectral integration strategy based on orthogonal polynomial expansions,which significantly alleviates stability constraints associated with conventional time-marching schemes.A fully connected neural network is employed to approximate the temperature-related variables,while governing equa-tions and boundary conditions are enforced through a physics-informed loss formulation.Numerical investigations demonstrate that the proposed method maintains high accuracy even when large time steps are adopted,where standard numerical solvers often suffer from instability or excessive computational cost.Moreover,the framework exhibits strong robustness for ultrathin configurations with extreme aspect ratios,achieving relative errors on the order of 10−5 or lower.These results indicate that the SINN framework provides a reliable and efficient alternative for transient thermal analysis of thin-walled structures under challenging computational conditions.
基金supported by National Key Research and Development Program of Chain(No.2021YFE0205300)National Natural Science Foundation of China(No.62171313).
文摘The future 6G networks will integrates space and terrestrial networks to realize a fully connected world with extensive collaboration.However,how to build trust between multiple parties is a difficult problem for secure cooperation without a reliable third-party.Blockchain is a promising technology to solve this problem by converting the trust between multi-parties to the trust to the common shared data.Several works have proposed to apply the incentive mechanism in blockchain to encourage effective cooperation,but how to evaluate the cooperation performance and avoid breach of contract is not discussed.In this paper,a secure relay scheme is proposed based on the consortium blockchain system composed by different operators.In particular,smart contract checks the integrity of the message based on RSA accumulator,and executes transactions automatically when the message is delivered successfully.Detailed procedures are introduced for both uplink and downlink relay.Implementation based on Hyperledger Fabric proves the effectiveness of the proposed scheme and shows that the complexity of the scheme is low enough for practical deployment.
基金This work was supported by the National Key Research Plan(2021YFB2900602).
文摘With the rapid development of low-orbit satellite com-munication networks both domestically and internationally,space-terrestrial integrated networks will become the future development trend.For space and terrestrial networks with limi-ted resources,the utilization efficiency of the entire space-terres-trial integrated networks resources can be affected by the core network indirectly.In order to improve the response efficiency of core networks expansion construction,early warning of the core network elements capacity is necessary.Based on the inte-grated architecture of space and terrestrial network,multidimen-sional factors are considered in this paper,including the number of terminals,login users,and the rules of users’migration during holidays.Using artifical intelligence(AI)technologies,the regis-tered users of the access and mobility management function(AMF),authorization users of the unified data management(UDM),protocol data unit(PDU)sessions of session manage-ment function(SMF)are predicted in combination with the num-ber of login users,the number of terminals.Therefore,the core network elements capacity can be predicted in advance.The proposed method is proven to be effective based on the data from real network.
文摘In December 2025,the ASEAN Centre for Energy(ACE)convened the third ASEAN Power Grid Partnership Meeting,bringing partners together for consultations on key issues.After more than two decades of planning and exploration,the ASEAN Power Grid is now entering a new phase—shifting from predominantly bilateral,one-way connections toward a multilateral,multidirectional network.
基金supported by the National Natural Science Foundation of China (Grant Nos.12192251,12334014,62335019,12134001,1230441812474378)+1 种基金the Quantum Science and Technology National Science and Technology Major Project(Grant No.2021ZD0301403)the Shanghai Municipal Science and Technology Major Project (Grant No.2019SHZDZX01)。
文摘Photonic neural networks(PNNs)of sufficiently large physical dimensions and high operation accuracies are envisaged as ideal candidates for breaking the major bottlenecks in the current artificial intelligence architectures in terms of latency,energy efficiency,and computational power.To achieve this vision,it is of vital importance to scale up the PNNs while simultaneously reducing the high demand on the dimensions required by them.The underlying cause of this strategy is the enormous gap between the scales of photonic and electronic integrated circuits.Here,we demonstrate monolithically integrated optical convolutional processors on thin film lithium niobate(TFLN)that harness inherent parallelism in photonics to enable large-scale programmable convolution kernels and,in turn,greatly reduce the dimensions required by subsequent fully connected layers.Experimental validation achieves high classification accuracies of 96%(86%)on the MNIST(Fashion-MNIST)dataset and 84.6%on the AG News dataset while dramatically reducing the required subsequent fully connected layer dimensions to 196×10(from 784×10)and 175×4(from 800×4),respectively.Furthermore,our devices can be driven by commercial field-programmable gate array systems;a unique advantage in addition to their scalable channel number and kernel size.Our architecture provides a solution to build practical machine learning photonic devices.
基金supported by the National Key Research&Development Program of China(No.2024YFB3910102).
文摘The rapid expansion of the low-altitude economy is driving strong demand for highly accurate and reliable positioning technologies to support diverse aerial operations.This review examines core positioning methodologies within the low-altitude intelligent network(LAIN)framework,beginning with an analysis of positioning requirements and performance metrics for low-altitude flight scenarios.It systematically assesses the principles,strengths,and limitations of mainstream positioning systems,including Global Navigation Satellite Systems(GNSS),terrestrial wireless positioning,and autonomous navigation,and it surveys prevalent integrated and cooperative positioning schemes.Our analysis demonstrates that standalone positioning technologies are inadequate in complex low-altitude settings,underscoring the pivotal role of multi-source fusion and unmanned aerial vehicle(UAV)swarm cooperative positioning as future trends.To address infrastructure gaps and high deployment costs in current LAIN systems,we propose a“space−air−ground”integrated and cooperative positioning architecture centered on GNSS and the 5th generation mobile communication technology(5G).The ground layer integrates 5G and GNSS for wide-area enhanced positioning.The aerial layer uses 5G aircraft-to-everything(A2X)and sidelink(SL)communications to build self-organizing networks for cooperative UAV localization.The space layer leverages low Earth orbit(LEO)satellites to overcome coverage limitations in communication and positioning.This hierarchical architecture reduces deployment costs through infrastructure reuse and enables deep integration of communication and navigation capabilities.By supporting collaborative enhancement across all three domains,the framework improves positioning robustness and delivers cost-effective,ubiquitous,and highly reliable positioning services.Finally,we outline promising research directions.This review aims to provide a systematic reference and a novel architectural perspective for the ongoing development of LAIN.
基金the North Dakota Industrial Commission (NDIC) for their financial supportprovided by the University of North Dakota Computational Research Center。
文摘Accurate estimation of mineralogy from geophysical well logs is crucial for characterizing geological formations,particularly in hydrocarbon exploration,CO_(2) sequestration,and geothermal energy development.Current techniques,such as multimineral petrophysical analysis,offer details into mineralogical distribution.However,it is inherently time-intensive and demands substantial geological expertise for accurate model evaluation.Furthermore,traditional machine learning techniques often struggle to predict mineralogy accurately and sometimes produce estimations that violate fundamental physical principles.To address this,we present a new approach using Physics-Integrated Neural Networks(PINNs),that combines data-driven learning with domain-specific physical constraints,embedding petrophysical relationships directly into the neural network architecture.This approach enforces that predictions adhere to physical laws.The methodology is applied to the Broom Creek Deep Saline aquifer,a CO_(2) sequestration site in the Williston Basin,to predict the volumes of key mineral constituents—quartz,dolomite,feldspar,anhydrite,illite—along with porosity.Compared to traditional artificial neural networks (ANN),the PINN approach demonstrates higher accuracy and better generalizability,significantly enhancing predictive performance on unseen well datasets.The average mean error across the three blind wells is 0.123 for ANN and 0.042 for PINN,highlighting the superior accuracy of the PINN approach.This method reduces uncertainties in reservoir characterization by improving the reliability of mineralogy and porosity predictions,providing a more robust tool for decision-making in various subsurface geoscience applications.
基金supported by the grants from the Key Research and Development Program of Xinjiang Uygur autonomous region in China(Grant No.2023B02017)the National Key Research and Development Program of China(Grant No.2024YFD2300703)+1 种基金the financial support from the Beijing Rural Revitalization Agricultural Science and Technology Project(Grant No.NY2401080000),BAIC01-2025the 2115 Talent Development Program of China Agricultural University.
文摘Phytomelatonin,an emerging plant hormone,plays vital roles in plant growth,development,and stress adaptation(Arnao et al.,2022;Ullah et al.,2024).It acts both as a direct antioxidant and a signaling molecule,engaging complex networks and interacting with other phytohormones(Liu et al.,2022;Khan et al.,2023).Although phytomelatonin receptors(PMTRs)have been identified in many plants(Wei et al.,2018;Wang et al.,2022;Liu et al.,2025),the downstream signaling mechanisms,particularly receptor-mediated protein modifications and transcriptional regulation,remain poorly characterized.
基金support from the National Key Research and Development Program of China(2024YFB3108400)the Hubei Province Key Research and Development Program(2024BAB051).
文摘Satellite-terrestrial networks have garnered significant attention in recent years and are extensively applied in intelligent transportation and emergency rescue.This paper provides a comprehensive review of the latest research advancements in satellite-terrestrial integrated network(STIN)technologies from a network perspective,dividing STIN technologies into three categories according to network service flows—namely,topology maintenance,network routing,and orchestration transmission technologies.Furthermore,a novel network-layer perspective is considered to examine the applications of STINs across various domains,along with related frameworks,platforms,simulators,and datasets.Finally,this paper explores the mainstream research directions in STIN technologies,with an innovative focus on the network layer.It reviews the existing literature,outlines future trends,and discusses opportunities for collaboration with related fields.
基金supported by China’s National Key R&D Program(Project Number:2022YFB2902100)。
文摘The sixth-generation(6G)networks will consist of multiple bands such as low-frequency,midfrequency,millimeter wave,terahertz and other bands to meet various business requirements and networking scenarios.The dynamic complementarity of multiple bands are crucial for enhancing the spectrum efficiency,reducing network energy consumption,and ensuring a consistent user experience.This paper investigates the present researches and challenges associated with deployment of multi-band integrated networks in existing infrastructures.Then,an evolutionary path for integrated networking is proposed with the consideration of maturity of emerging technologies and practical network deployment.The proposed design principles for 6G multi-band integrated networking aim to achieve on-demand networking objectives,while the architecture supports full spectrum access and collaboration between high and low frequencies.In addition,the potential key air interface technologies and intelligent technologies for integrated networking are comprehensively discussed.It will be a crucial basis for the subsequent standards promotion of 6G multi-band integrated networking technology.
基金supported in part by the National Natural Science Foundation of China under Grant 62171020.
文摘Blockchain-based spectrum sharing with consensus is the key technology for sixth-generation mobile communication to realize dynamic spectrum management.In order to avoid the waste of computing and communication resources,a spectrum sharing policy-based consensus mechanism is proposed in this paper.Firstly,a spectrum sharing algorithm based on graph neural network is designed in the satelliteterrestrial spectrum sharing networks under the underlay model.It avoids high computational overhead of the traditional iterative optimization algorithm when the wireless channel condition and network topology are highly dynamic.Secondly,a consensus mechanism based on spectrum sharing strategy is designed,which converts the traditional meaningless hash problem into the graph neural network training.Miners compete for accounting rights by training a graph neutral network model that meets the spectrum sharing requirement.Finally,the consensus delay,communication and storage overhead of the proposed consensus mechanism are analyzed theoretically.The simulation results show that the proposed consensus mechanism can effectively improve spectrum efficiency with excellent scalability and generalization performance.
基金National Key Research and Development Program of China(2022YFE0139300)Hubei Province Key Research and Development Program(2024BAB051)+1 种基金Guangdong Basic and Applied Basic Research Foundation(2022B1515120067)Wuhan Key Research and Development Program(2024050702030136).
文摘To support ubiquitous communication and enhance other 6G applications,the Space-Air-Ground Integrated Network(SAGIN)has become a research hotspot.Traditionally,satellite-ground fusion technologies integrate network entities from space,aerial,and terrestrial domains.However,they face challenges such as spectrum scarcity and inefficient satellite handover.This paper explores the Channel-Aware Handover Management(CAHM)strategy in SAGIN for data allocation.Specifically,CAHM utilizes the data receiving capability of Low Earth Orbit(LEO)satellites,considering satellite-ground distance,free-space path loss,and channel gain.Furthermore,CAHM assesses LEO satellite data forwarding capability using signal-to-noise ratio,link duration and buffer queue length.Then,CAHM applies historical data on LEO satellite transmission successes and failures to effectively reduce overall interruption ratio.Simulation results show that CAHM outperforms baseline algorithms in terms of delivery ratio,latency,and interruption ratio.
基金supported by the National Key Research and Development Program of China under Grant No.2022YFB2902501the Fundamental Research Funds for the Central Universities under Grant No.2023ZCJH09the Haidian District Golden Bridge Seed Fund of Beijing Municipality under Grant No.S2024161.
文摘In recent years,load balancing routing al-gorithms have been extensively studied in satellite net-works.Most existing studies focus on path selection and hop-count optimization for end-to-end transmis-sion,while overlooking congestion issues on feeder links caused by the limited number and centralized distribution of ground stations.Hence,a multi-service routing algorithm called the Multi-service Load Bal-ancing Routing Algorithm for Traffic Return(MLB-TR)is proposed.Unlike traditional approaches,MLB-TR aims to achieve a broader and more comprehensive load balancing objective.Specifically,based on the service type,an appropriate landing satellite is first selected by considering factors such as shortest path hop count and satellite load.Then,a set of candidate paths from the source satellite to the selected landing satellite is computed.Finally,using the regional load balancing index as the optimization objective,the final transmission path is selected from the candidate path set.Simulation results show that the proposed algo-rithm outperforms the existing works.
基金supported by the Shanghai Municipal Science and Technology Major Project, the Science and Technology Commission of Shanghai Municipality (No. 21DZ1100500)the Shanghai Frontiers Science Center Program (2021-2025 No. 20)+2 种基金the National Key Research and Development Program of China (No. 2021YFB2802000)the National Natural Science Foundation of China (No. 61975123, No. 62305217),the National Natural Science Foundation of China (No. 52075504)the Shanghai Pujiang Programme
文摘Rising demands for bandwidth,speed,and energy efficiency are reshaping the landscape of computing beyond the limits of von Neumann electronics.Neuromorphic photonics—using light to emulate neural computation—offers ultrafast,massively parallel,and low-energy information processing,positioning integrated photonic neural networks(IPNNs)as promising hardware for next-generation artificial intelligence(AI).By combining the architectural efficiency of neuromorphic models with the physical advantages of integrated photonics,IPNNs enable high-speed and programmable linear operations during the in-plane optical transmission,while leaving room for compact and reconfigurable on-chip optical nonlinearities and memory functions.Firstly,we review the concepts and principles of key building blocks in IPNN,that are photonic synapses,neurons,and photonic memristors which offer optical memory and storage capabilities.And then,we summarize the representative IPNN architectures and their recent advances,including coherent,parallel,diffractive,and reservoir computing,for photonic neuromorphic computing with high throughput and high efficiency.Finally,we outline practical considerations—calibration and stability of large-scale networks,routes toward co-integration with electronics,diffractive–interferometric hybrid architectures,and programmable photonic architectures for general AI purposes.We highlight a forward outlook on enabling IPNN with low energy consumption,robust photonic operations,and efficient training strategies,aiming to guide the maturation of general-purpose,low-power photonic AI.
基金funded by State Key Laboratory of Micro-Spacecraft Rapid Design and Intelligent Cluster under Grant MS01240103the National Natural Science Foundation of China under Grant 62071146National 2011 Collaborative Innovation Center of Wireless Communication Technologies under Grant 2242022k60006.
文摘In this paper,we propose a joint power and frequency allocation algorithm considering interference protection in the integrated satellite and terrestrial network(ISTN).We efficiently utilize spectrum resources by allowing user equipment(UE)of terrestrial networks to share frequencies with satellite networks.In order to protect the satellite terminal(ST),the base station(BS)needs to control the transmit power and frequency resources of the UE.The optimization problem involves maximizing the achievable throughput while satisfying the interference protection constraints of the ST and the quality of service(QoS)of the UE.However,this problem is highly nonconvex,and we decompose it into power allocation and frequency resource scheduling subproblems.In the power allocation subproblem,we propose a power allocation algorithm based on interference probability(PAIP)to address channel uncertainty.We obtain the suboptimal power allocation solution through iterative optimization.In the frequency resource scheduling subproblem,we develop a heuristic algorithm to handle the non-convexity of the problem.The simulation results show that the combination of power allocation and frequency resource scheduling algorithms can improve spectrum utilization.
基金supported in part by the Major Program of the National Natural Science Foundation of China(62495021 and 62495020).
文摘The rapid growth of low-Earth-orbit satellites has injected new vitality into future service provisioning.However,given the inherent volatility of network traffic,ensuring differentiated quality of service in highly dynamic networks remains a significant challenge.In this paper,we propose an online learning-based resource scheduling scheme for satellite-terrestrial integrated networks(STINs)aimed at providing on-demand services with minimal resource utilization.Specifically,we focus on:①accurately characterizing the STIN channel,②predicting resource demand with uncertainty guarantees,and③implementing mixed timescale resource scheduling.For the STIN channel,we adopt the 3rd Generation Partnership Project channel and antenna models for non-terrestrial networks.We employ a one-dimensional convolution and attention-assisted long short-term memory architecture for average demand prediction,while introducing conformal prediction to mitigate uncertainties arising from burst traffic.Additionally,we develop a dual-timescale optimization framework that includes resource reservation on a larger timescale and resource adjustment on a smaller timescale.We also designed an online resource scheduling algorithm based on online convex optimization to guarantee long-term performance with limited knowledge of time-varying network information.Based on the Network Simulator 3 implementation of the STIN channel under our high-fidelity satellite Internet simulation platform,numerical results using a real-world dataset demonstrate the accuracy and efficiency of the prediction algorithms and online resource scheduling scheme.
基金Supported by the Self-funded Research Project of Beijing FibrLink Communications Co.Ltd.“Research on Key Technologies forUnifiedManagement of Air-to-Earth Integrated CommunicationNetworks(546826230034).”。
文摘The lack of communication infrastructure in remote regions presents significant obstacles to gathering data from smart power sensors(SPSs)in smart grid networks.In such cases,a space-air-ground integrated network serves as an effective emergency solution.This study addresses the challenge of optimizing the energy efficiency of data transmission fromSPSs to low Earth orbit(LEO)satellites through unmanned aerial vehicles(UAVs),considering both effective capacity and fronthaul link capacity constraints.Due to the non-convex nature of the problem,the objective function is reformulated,and a delay-aware energy-efficient power allocation and UAV trajectory design(DEPATD)algorithm is proposed as a two-loop approach.Since the inner loop remains non-convex,the block coordinate descent(BCD)method is employed to decompose it into three subproblems:power allocation for SPSs,power allocation for UAVs,and UAV trajectory design.The first two subproblems are solved using the Lagrangian dual method,while the third is addressed with the successive convex approximation(SCA)technique.By iteratively solving these subproblems,an efficient algorithm is developed to resolve the inner loop issue.Simulation results demonstrate that the energy efficiency of the proposed DEPATD algorithm improves by 4.02% compared to the benchmark algorithm when the maximum transmission power of the SPSs increases from 0.1 to 0.45W.
