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
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
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
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
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 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
1.Introduction As a key development of the next-generation spatial information infrastructure,1the Satellite-Terrestrial Integrated Network(STIN)has become a strategic priority actively pursued by major spacefaring na...1.Introduction As a key development of the next-generation spatial information infrastructure,1the Satellite-Terrestrial Integrated Network(STIN)has become a strategic priority actively pursued by major spacefaring nations and regions,including the United States,Europe,China,and Russia.Specifically,Space X’s Starlink project has deployed over 6750 satellites,2while One Web has completed its initial phase of satellite constellation deployment with more than 600 satellites.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
Low-carbon smart parks achieve selfbalanced carbon emission and absorption through the cooperative scheduling of direct current(DC)-based distributed photovoltaic,energy storage units,and loads.Direct current power li...Low-carbon smart parks achieve selfbalanced carbon emission and absorption through the cooperative scheduling of direct current(DC)-based distributed photovoltaic,energy storage units,and loads.Direct current power line communication(DC-PLC)enables real-time data transmission on DC power lines.With traffic adaptation,DC-PLC can be integrated with other complementary media such as 5G to reduce transmission delay and improve reliability.However,traffic adaptation for DC-PLC and 5G integration still faces the challenges such as coupling between traffic admission control and traffic partition,dimensionality curse,and the ignorance of extreme event occurrence.To address these challenges,we propose a deep reinforcement learning(DRL)-based delay sensitive and reliable traffic adaptation algorithm(DSRTA)to minimize the total queuing delay under the constraints of traffic admission control,queuing delay,and extreme events occurrence probability.DSRTA jointly optimizes traffic admission control and traffic partition,and enables learning-based intelligent traffic adaptation.The long-term constraints are incorporated into both state and bound of drift-pluspenalty to achieve delay awareness and enforce reliability guarantee.Simulation results show that DSRTA has lower queuing delay and more reliable quality of service(QoS)guarantee than other state-of-the-art algorithms.展开更多
With the rapid growth of connected devices,traditional edge-cloud systems are under overload pressure.Using mobile edge computing(MEC)to assist unmanned aerial vehicles(UAVs)as low altitude platform stations(LAPS)for ...With the rapid growth of connected devices,traditional edge-cloud systems are under overload pressure.Using mobile edge computing(MEC)to assist unmanned aerial vehicles(UAVs)as low altitude platform stations(LAPS)for communication and computation to build air-ground integrated networks(AGINs)offers a promising solution for seamless network coverage of remote internet of things(IoT)devices in the future.To address the performance demands of future mobile devices(MDs),we proposed an MEC-assisted AGIN system.The goal is to minimize the long-term computational overhead of MDs by jointly optimizing transmission power,flight trajecto-ries,resource allocation,and offloading ratios,while utilizing non-orthogonal multiple access(NOMA)to improve device connectivity of large-scale MDs and spectral efficiency.We first designed an adaptive clustering scheme based on K-Means to cluster MDs and established commu-nication links,improving efficiency and load balancing.Then,considering system dynamics,we introduced a partial computation offloading algorithm based on multi-agent deep deterministic pol-icy gradient(MADDPG),modeling the multi-UAV computation offloading problem as a Markov decision process(MDP).This algorithm optimizes resource allocation through centralized training and distributed execution,reducing computational overhead.Simulation results show that the pro-posed algorithm not only converges stably but also outperforms other benchmark algorithms in han-dling complex scenarios with multiple devices.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
Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently...Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently,enhancing the robustness of scale-free networks has become a pressing issue.To address this problem,this paper proposes a Multi-Granularity Integration Algorithm(MGIA),which aims to improve the robustness of scale-free networks while keeping the initial degree of each node unchanged,ensuring network connectivity and avoiding the generation of multiple edges.The algorithm generates a multi-granularity structure from the initial network to be optimized,then uses different optimization strategies to optimize the networks at various granular layers in this structure,and finally realizes the information exchange between different granular layers,thereby further enhancing the optimization effect.We propose new network refresh,crossover,and mutation operators to ensure that the optimized network satisfies the given constraints.Meanwhile,we propose new network similarity and network dissimilarity evaluation metrics to improve the effectiveness of the optimization operators in the algorithm.In the experiments,the MGIA enhances the robustness of the scale-free network by 67.6%.This improvement is approximately 17.2%higher than the optimization effects achieved by eight currently existing complex network robustness optimization algorithms.展开更多
The rise of large-scale artificial intelligence(AI)models,such as ChatGPT,Deep-Seek,and autonomous vehicle systems,has significantly advanced the boundaries of AI,enabling highly complex tasks in natural language proc...The rise of large-scale artificial intelligence(AI)models,such as ChatGPT,Deep-Seek,and autonomous vehicle systems,has significantly advanced the boundaries of AI,enabling highly complex tasks in natural language processing,image recognition,and real-time decisionmaking.However,these models demand immense computational power and are often centralized,relying on cloud-based architectures with inherent limitations in latency,privacy,and energy efficiency.To address these challenges and bring AI closer to real-world applications,such as wearable health monitoring,robotics,and immersive virtual environments,innovative hardware solutions are urgently needed.This work introduces a near-sensor edge computing(NSEC)system,built on a bilayer AlN/Si waveguide platform,to provide real-time,energy-efficient AI capabilities at the edge.Leveraging the electro-optic properties of AlN microring resonators for photonic feature extraction,coupled with Si-based thermo-optic Mach-Zehnder interferometers for neural network computations,the system represents a transformative approach to AI hardware design.Demonstrated through multimodal gesture and gait analysis,the NSEC system achieves high classification accuracies of 96.77%for gestures and 98.31%for gaits,ultra-low latency(<10 ns),and minimal energy consumption(<0.34 pJ).This groundbreaking system bridges the gap between AI models and real-world applications,enabling efficient,privacy-preserving AI solutions for healthcare,robotics,and next-generation human-machine interfaces,marking a pivotal advancement in edge computing and AI deployment.展开更多
In unmanned aerial vehicle(UAV)networks,the high mobility of nodes leads to frequent changes in network topology,which brings challenges to the neighbor discovery(ND)for UAV networks.Integrated sensing and communicati...In unmanned aerial vehicle(UAV)networks,the high mobility of nodes leads to frequent changes in network topology,which brings challenges to the neighbor discovery(ND)for UAV networks.Integrated sensing and communication(ISAC),as an emerging technology in 6G mobile networks,has shown great potential in improving communication performance with the assistance of sensing information.ISAC obtains the prior information about node distribution,reducing the ND time.However,the prior information obtained through ISAC may be imperfect.Hence,an ND algorithm based on reinforcement learning is proposed.The learning automaton(LA)is applied to interact with the environment and continuously adjust the probability of selecting beams to accelerate the convergence speed of ND algorithms.Besides,an efficient ND algorithm in the neighbor maintenance phase is designed,which applies the Kalman filter to predict node movement.Simulation results show that the LA-based ND algorithm reduces the ND time by up to 32%compared with the Scan-Based Algorithm(SBA),which proves the efficiency of the proposed ND algorithms.展开更多
In this paper, we investigate a cooperation mechanism for satellite-terrestrial integrated networks. The terrestrial relays act as the supplement of traditional small cells and cooperatively provide seamless coverage ...In this paper, we investigate a cooperation mechanism for satellite-terrestrial integrated networks. The terrestrial relays act as the supplement of traditional small cells and cooperatively provide seamless coverage for users in the densely populated areas.To deal with the dynamic satellite backhaul links and backhaul capacity caused by the satellite mobility, severe co-channel interference in both satellite backhaul links and user links introduced by spectrum sharing,and the difference demands of users as well as heterogeneous characteristics of terrestrial backhaul and satellite backhaul, we propose a joint user association and satellite selection scheme to maximize the total sum rate. The optimization problem is formulated via jointly considering the influence of dynamic backhaul links, individual requirements and targeted interference management strategies, which is decomposed into two subproblems: user association and satellite selection. The user association is formulated as a nonconvex optimization problem, and solved through a low-complexity heuristic scheme to find the most suitable access point serving each user. Then, the satellite selection is resolved based on the cooperation among terrestrial relays to maximize the total backhaul capacity with the minimum date rate constraints. Finally,simulation results show the effectiveness of the proposed scheme in terms of total sum rate and power efficiency of TRs' backhaul.展开更多
The integrated simulation and optimization technology of reservoir-wellbore-pipe network is developed to reflect the mutual influence and restriction among reservoir engineering,oil production engineering and surface ...The integrated simulation and optimization technology of reservoir-wellbore-pipe network is developed to reflect the mutual influence and restriction among reservoir engineering,oil production engineering and surface engineering,and to obtain the scheme with minimum conflict and optimal benefit in each step.This technology is based on the concept of global optimization to maximize production and profit,reduce costs and increase benefit.This paper elaborates the current situation of integrated simulation technology of reservoir-wellbore-pipe network both at home and abroad,discusses its correlation with the primary business of Sinopec and its development from three aspects of modeling,cloud platform and intellectualization.Suggestions on its future development are put forward from underlying data,software platform,popularization and application,and cross-border integration to provide means and guidance for the construction of intelligent oil and gas fields.The results show that the integrated simulation of reservoir-wellbore-pipe network can better reflect the optimization requirements of each step,avoid the ineffective operation of field equipment,and effectively improve the efficiency of research and management.Coupling solution,global optimization method and pressure fitting,which can make the simulation results reflect the real situation,are the key technologies for the network.The theoretical technology and main function research of integrated simulation technology have been mature,but the large-scale application and local function improvement of oil and gas fields are yet to be promoted.In the future,the integrated simulation of reservoir-wellbore-pipe network will develop from digitalization to modeling and intellectualization,from local simulation to cloud computing,and from manual intervention to intelligent decision-making.We suggest speeding up the construction of the unified database and model base of the whole underlying platform,strengthening the construction of software integration and integration platform with independent intellectual property rights,speeding up the popularization and application of intelligent oil and gas field demonstration projects,and strengthening the integration of oil and gas industry with artificial intelligence(AI),big data and block chain for its development.展开更多
In today's world where everything is interconnected, air-space-ground integrated networks have become a current research hotspot due to their characteristics of high, long and wide area coverage. Given the constan...In today's world where everything is interconnected, air-space-ground integrated networks have become a current research hotspot due to their characteristics of high, long and wide area coverage. Given the constantly changing and dynamic characteristics of air and space networks, along with the sheer number and complexity of access nodes involved, the process of rapid networking presents substantial challenges. In order to achieve rapid and dynamic networking of air-space-ground integrated networks, this paper focuses on the study of methods for large-scale nodes to randomly access satellites. This paper utilizes a cross-layer design methodology to enhance the access success probability by jointly optimizing the physical layer and medium access control(MAC) layer aspects. Load statistics priority random access(LSPRA) technology is proposed.Experiments show that when the number of nodes is greater than 1 000, this method can also ensure stable access performance, providing ideas for the design of air-space-ground integrated network access systems.展开更多
Integrated satellite unmanned aerial vehicle relay networks(ISUAVRNs)have become a prominent topic in recent years.This paper investigates the average secrecy capacity(ASC)for reconfigurable intelligent surface(RIS)-e...Integrated satellite unmanned aerial vehicle relay networks(ISUAVRNs)have become a prominent topic in recent years.This paper investigates the average secrecy capacity(ASC)for reconfigurable intelligent surface(RIS)-enabled ISUAVRNs.Especially,an eve is considered to intercept the legitimate information from the considered secrecy system.Besides,we get detailed expressions for the ASC of the regarded secrecy system with the aid of the reconfigurable intelligent.Furthermore,to gain insightful results of the major parameters on the ASC in high signalto-noise ratio regime,the approximate investigations are further gotten,which give an efficient method to value the secrecy analysis.At last,some representative computer results are obtained to prove the theoretical findings.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.
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 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 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.
基金co-supported by the National Natural Science Foundation of China(Nos.62225103,U2441227,U24A20211)the Fundamental Research Funds for the Central Universities of China(No.FRF-TP-22-002C2)。
文摘1.Introduction As a key development of the next-generation spatial information infrastructure,1the Satellite-Terrestrial Integrated Network(STIN)has become a strategic priority actively pursued by major spacefaring nations and regions,including the United States,Europe,China,and Russia.Specifically,Space X’s Starlink project has deployed over 6750 satellites,2while One Web has completed its initial phase of satellite constellation deployment with more than 600 satellites.
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 by the Science and Technology Project of State Grid Corporation of China under grant 52094021N010(5400-202199534A-0-5-ZN)。
文摘Low-carbon smart parks achieve selfbalanced carbon emission and absorption through the cooperative scheduling of direct current(DC)-based distributed photovoltaic,energy storage units,and loads.Direct current power line communication(DC-PLC)enables real-time data transmission on DC power lines.With traffic adaptation,DC-PLC can be integrated with other complementary media such as 5G to reduce transmission delay and improve reliability.However,traffic adaptation for DC-PLC and 5G integration still faces the challenges such as coupling between traffic admission control and traffic partition,dimensionality curse,and the ignorance of extreme event occurrence.To address these challenges,we propose a deep reinforcement learning(DRL)-based delay sensitive and reliable traffic adaptation algorithm(DSRTA)to minimize the total queuing delay under the constraints of traffic admission control,queuing delay,and extreme events occurrence probability.DSRTA jointly optimizes traffic admission control and traffic partition,and enables learning-based intelligent traffic adaptation.The long-term constraints are incorporated into both state and bound of drift-pluspenalty to achieve delay awareness and enforce reliability guarantee.Simulation results show that DSRTA has lower queuing delay and more reliable quality of service(QoS)guarantee than other state-of-the-art algorithms.
基金supported by the Gansu Province Key Research and Development Plan(No.23YFGA0062)Gansu Provin-cial Innovation Fund(No.2022A-215).
文摘With the rapid growth of connected devices,traditional edge-cloud systems are under overload pressure.Using mobile edge computing(MEC)to assist unmanned aerial vehicles(UAVs)as low altitude platform stations(LAPS)for communication and computation to build air-ground integrated networks(AGINs)offers a promising solution for seamless network coverage of remote internet of things(IoT)devices in the future.To address the performance demands of future mobile devices(MDs),we proposed an MEC-assisted AGIN system.The goal is to minimize the long-term computational overhead of MDs by jointly optimizing transmission power,flight trajecto-ries,resource allocation,and offloading ratios,while utilizing non-orthogonal multiple access(NOMA)to improve device connectivity of large-scale MDs and spectral efficiency.We first designed an adaptive clustering scheme based on K-Means to cluster MDs and established commu-nication links,improving efficiency and load balancing.Then,considering system dynamics,we introduced a partial computation offloading algorithm based on multi-agent deep deterministic pol-icy gradient(MADDPG),modeling the multi-UAV computation offloading problem as a Markov decision process(MDP).This algorithm optimizes resource allocation through centralized training and distributed execution,reducing computational overhead.Simulation results show that the pro-posed algorithm not only converges stably but also outperforms other benchmark algorithms in han-dling complex scenarios with multiple devices.
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National Natural Science Foundation of China(11971211,12171388).
文摘Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently,enhancing the robustness of scale-free networks has become a pressing issue.To address this problem,this paper proposes a Multi-Granularity Integration Algorithm(MGIA),which aims to improve the robustness of scale-free networks while keeping the initial degree of each node unchanged,ensuring network connectivity and avoiding the generation of multiple edges.The algorithm generates a multi-granularity structure from the initial network to be optimized,then uses different optimization strategies to optimize the networks at various granular layers in this structure,and finally realizes the information exchange between different granular layers,thereby further enhancing the optimization effect.We propose new network refresh,crossover,and mutation operators to ensure that the optimized network satisfies the given constraints.Meanwhile,we propose new network similarity and network dissimilarity evaluation metrics to improve the effectiveness of the optimization operators in the algorithm.In the experiments,the MGIA enhances the robustness of the scale-free network by 67.6%.This improvement is approximately 17.2%higher than the optimization effects achieved by eight currently existing complex network robustness optimization algorithms.
基金the National Research Foundation(NRF)Singapore mid-sized center grant(NRF-MSG-2023-0002)FrontierCRP grant(NRF-F-CRP-2024-0006)+2 种基金A*STAR Singapore MTC RIE2025 project(M24W1NS005)IAF-PP project(M23M5a0069)Ministry of Education(MOE)Singapore Tier 2 project(MOE-T2EP50220-0014).
文摘The rise of large-scale artificial intelligence(AI)models,such as ChatGPT,Deep-Seek,and autonomous vehicle systems,has significantly advanced the boundaries of AI,enabling highly complex tasks in natural language processing,image recognition,and real-time decisionmaking.However,these models demand immense computational power and are often centralized,relying on cloud-based architectures with inherent limitations in latency,privacy,and energy efficiency.To address these challenges and bring AI closer to real-world applications,such as wearable health monitoring,robotics,and immersive virtual environments,innovative hardware solutions are urgently needed.This work introduces a near-sensor edge computing(NSEC)system,built on a bilayer AlN/Si waveguide platform,to provide real-time,energy-efficient AI capabilities at the edge.Leveraging the electro-optic properties of AlN microring resonators for photonic feature extraction,coupled with Si-based thermo-optic Mach-Zehnder interferometers for neural network computations,the system represents a transformative approach to AI hardware design.Demonstrated through multimodal gesture and gait analysis,the NSEC system achieves high classification accuracies of 96.77%for gestures and 98.31%for gaits,ultra-low latency(<10 ns),and minimal energy consumption(<0.34 pJ).This groundbreaking system bridges the gap between AI models and real-world applications,enabling efficient,privacy-preserving AI solutions for healthcare,robotics,and next-generation human-machine interfaces,marking a pivotal advancement in edge computing and AI deployment.
基金supported in part by the Fundamental Research Funds for the Central Universities under Grant No.2024ZCJH01in part by the National Natural Science Foundation of China(NSFC)under Grant No.62271081in part by the National Key Research and Development Program of China under Grant No.2020YFA0711302.
文摘In unmanned aerial vehicle(UAV)networks,the high mobility of nodes leads to frequent changes in network topology,which brings challenges to the neighbor discovery(ND)for UAV networks.Integrated sensing and communication(ISAC),as an emerging technology in 6G mobile networks,has shown great potential in improving communication performance with the assistance of sensing information.ISAC obtains the prior information about node distribution,reducing the ND time.However,the prior information obtained through ISAC may be imperfect.Hence,an ND algorithm based on reinforcement learning is proposed.The learning automaton(LA)is applied to interact with the environment and continuously adjust the probability of selecting beams to accelerate the convergence speed of ND algorithms.Besides,an efficient ND algorithm in the neighbor maintenance phase is designed,which applies the Kalman filter to predict node movement.Simulation results show that the LA-based ND algorithm reduces the ND time by up to 32%compared with the Scan-Based Algorithm(SBA),which proves the efficiency of the proposed ND algorithms.
基金supported by National Natural Science Foundation of China (No. 62201593, 62471480, and 62171466)。
文摘In this paper, we investigate a cooperation mechanism for satellite-terrestrial integrated networks. The terrestrial relays act as the supplement of traditional small cells and cooperatively provide seamless coverage for users in the densely populated areas.To deal with the dynamic satellite backhaul links and backhaul capacity caused by the satellite mobility, severe co-channel interference in both satellite backhaul links and user links introduced by spectrum sharing,and the difference demands of users as well as heterogeneous characteristics of terrestrial backhaul and satellite backhaul, we propose a joint user association and satellite selection scheme to maximize the total sum rate. The optimization problem is formulated via jointly considering the influence of dynamic backhaul links, individual requirements and targeted interference management strategies, which is decomposed into two subproblems: user association and satellite selection. The user association is formulated as a nonconvex optimization problem, and solved through a low-complexity heuristic scheme to find the most suitable access point serving each user. Then, the satellite selection is resolved based on the cooperation among terrestrial relays to maximize the total backhaul capacity with the minimum date rate constraints. Finally,simulation results show the effectiveness of the proposed scheme in terms of total sum rate and power efficiency of TRs' backhaul.
基金funded by the SINOPEC Science and Technology Project(No.P18080).
文摘The integrated simulation and optimization technology of reservoir-wellbore-pipe network is developed to reflect the mutual influence and restriction among reservoir engineering,oil production engineering and surface engineering,and to obtain the scheme with minimum conflict and optimal benefit in each step.This technology is based on the concept of global optimization to maximize production and profit,reduce costs and increase benefit.This paper elaborates the current situation of integrated simulation technology of reservoir-wellbore-pipe network both at home and abroad,discusses its correlation with the primary business of Sinopec and its development from three aspects of modeling,cloud platform and intellectualization.Suggestions on its future development are put forward from underlying data,software platform,popularization and application,and cross-border integration to provide means and guidance for the construction of intelligent oil and gas fields.The results show that the integrated simulation of reservoir-wellbore-pipe network can better reflect the optimization requirements of each step,avoid the ineffective operation of field equipment,and effectively improve the efficiency of research and management.Coupling solution,global optimization method and pressure fitting,which can make the simulation results reflect the real situation,are the key technologies for the network.The theoretical technology and main function research of integrated simulation technology have been mature,but the large-scale application and local function improvement of oil and gas fields are yet to be promoted.In the future,the integrated simulation of reservoir-wellbore-pipe network will develop from digitalization to modeling and intellectualization,from local simulation to cloud computing,and from manual intervention to intelligent decision-making.We suggest speeding up the construction of the unified database and model base of the whole underlying platform,strengthening the construction of software integration and integration platform with independent intellectual property rights,speeding up the popularization and application of intelligent oil and gas field demonstration projects,and strengthening the integration of oil and gas industry with artificial intelligence(AI),big data and block chain for its development.
基金supported by the National Natural Science Foundation of China (No. 62027801)。
文摘In today's world where everything is interconnected, air-space-ground integrated networks have become a current research hotspot due to their characteristics of high, long and wide area coverage. Given the constantly changing and dynamic characteristics of air and space networks, along with the sheer number and complexity of access nodes involved, the process of rapid networking presents substantial challenges. In order to achieve rapid and dynamic networking of air-space-ground integrated networks, this paper focuses on the study of methods for large-scale nodes to randomly access satellites. This paper utilizes a cross-layer design methodology to enhance the access success probability by jointly optimizing the physical layer and medium access control(MAC) layer aspects. Load statistics priority random access(LSPRA) technology is proposed.Experiments show that when the number of nodes is greater than 1 000, this method can also ensure stable access performance, providing ideas for the design of air-space-ground integrated network access systems.
基金the National Natural Science Foundation of China under Grants 62001517 and 61971474the Beijing Nova Program under Grant Z201100006820121.
文摘Integrated satellite unmanned aerial vehicle relay networks(ISUAVRNs)have become a prominent topic in recent years.This paper investigates the average secrecy capacity(ASC)for reconfigurable intelligent surface(RIS)-enabled ISUAVRNs.Especially,an eve is considered to intercept the legitimate information from the considered secrecy system.Besides,we get detailed expressions for the ASC of the regarded secrecy system with the aid of the reconfigurable intelligent.Furthermore,to gain insightful results of the major parameters on the ASC in high signalto-noise ratio regime,the approximate investigations are further gotten,which give an efficient method to value the secrecy analysis.At last,some representative computer results are obtained to prove the theoretical findings.
