Effectively handling imbalanced datasets remains a fundamental challenge in computational modeling and machine learning,particularly when class overlap significantly deteriorates classification performance.Traditional...Effectively handling imbalanced datasets remains a fundamental challenge in computational modeling and machine learning,particularly when class overlap significantly deteriorates classification performance.Traditional oversampling methods often generate synthetic samples without considering density variations,leading to redundant or misleading instances that exacerbate class overlap in high-density regions.To address these limitations,we propose Wasserstein Generative Adversarial Network Variational Density Estimation WGAN-VDE,a computationally efficient density-aware adversarial resampling framework that enhances minority class representation while strategically reducing class overlap.The originality of WGAN-VDE lies in its density-aware sample refinement,ensuring that synthetic samples are positioned in underrepresented regions,thereby improving class distinctiveness.By applying structured feature representation,targeted sample generation,and density-based selection mechanisms strategies,the proposed framework ensures the generation of well-separated and diverse synthetic samples,improving class separability and reducing redundancy.The experimental evaluation on 20 benchmark datasets demonstrates that this approach outperforms 11 state-of-the-art rebalancing techniques,achieving superior results in F1-score,Accuracy,G-Mean,and AUC metrics.These results establish the proposed method as an effective and robust computational approach,suitable for diverse engineering and scientific applications involving imbalanced data classification and computational modeling.展开更多
The rapid evolution of wireless technologies and the advent of 6G networks present new challenges and opportunities for Internet ofThings(IoT)applications,particularly in terms of ultra-reliable,secure,and energyeffic...The rapid evolution of wireless technologies and the advent of 6G networks present new challenges and opportunities for Internet ofThings(IoT)applications,particularly in terms of ultra-reliable,secure,and energyefficient communication.This study explores the integration of Reconfigurable Intelligent Surfaces(RIS)into IoT networks to enhance communication performance.Unlike traditional passive reflector-based approaches,RIS is leveraged as an active optimization tool to improve both backscatter and direct communication modes,addressing critical IoT challenges such as energy efficiency,limited communication range,and double-fading effects in backscatter communication.We propose a novel computational framework that combines RIS functionality with Physical Layer Security(PLS)mechanisms,optimized through the algorithm known as Deep Deterministic Policy Gradient(DDPG).This framework adaptively adapts RIS configurations and transmitter beamforming to reduce key challenges,including imperfect channel state information(CSI)and hardware limitations like quantized RIS phase shifts.By optimizing both RIS settings and beamforming in real-time,our approach outperforms traditional methods by significantly increasing secrecy rates,improving spectral efficiency,and enhancing energy efficiency.Notably,this framework adapts more effectively to the dynamic nature of wireless channels compared to conventional optimization techniques,providing scalable solutions for large-scale RIS deployments.Our results demonstrate substantial improvements in communication performance setting a new benchmark for secure,efficient and scalable 6G communication.This work offers valuable insights for the future of IoT networks,with a focus on computational optimization,high spectral efficiency and energy-aware operations.展开更多
In the field of edge computing,achieving low-latency computational task offloading with limited resources is a critical research challenge,particularly in resource-constrained and latency-sensitive vehicular network e...In the field of edge computing,achieving low-latency computational task offloading with limited resources is a critical research challenge,particularly in resource-constrained and latency-sensitive vehicular network environments where rapid response is mandatory for safety-critical applications.In scenarios where edge servers are sparsely deployed,the lack of coordination and information sharing often leads to load imbalance,thereby increasing system latency.Furthermore,in regions without edge server coverage,tasks must be processed locally,which further exacerbates latency issues.To address these challenges,we propose a novel and efficient Deep Reinforcement Learning(DRL)-based approach aimed at minimizing average task latency.The proposed method incorporates three offloading strategies:local computation,direct offloading to the edge server in local region,and device-to-device(D2D)-assisted offloading to edge servers in other regions.We formulate the task offloading process as a complex latency minimization optimization problem.To solve it,we propose an advanced algorithm based on the Dueling Double Deep Q-Network(D3QN)architecture and incorporating the Prioritized Experience Replay(PER)mechanism.Experimental results demonstrate that,compared with existing offloading algorithms,the proposed method significantly reduces average task latency,enhances user experience,and offers an effective strategy for latency optimization in future edge computing systems under dynamic workloads.展开更多
Federated learning often experiences slow and unstable convergence due to edge-side data heterogeneity.This problem becomes more severe when edge participation rate is low,as the information collected from different e...Federated learning often experiences slow and unstable convergence due to edge-side data heterogeneity.This problem becomes more severe when edge participation rate is low,as the information collected from different edge devices varies significantly.As a result,communication overhead increases,which further slows down the convergence process.To address this challenge,we propose a simple yet effective federated learning framework that improves consistency among edge devices.The core idea is clusters the lookahead gradients collected from edge devices on the cloud server to obtain personalized momentum for steering local updates.In parallel,a global momentum is applied during model aggregation,enabling faster convergence while preserving personalization.This strategy enables efficient propagation of the estimated global update direction to all participating edge devices and maintains alignment in local training,without introducing extra memory or communication overhead.We conduct extensive experiments on benchmark datasets such as Cifar100 and Tiny-ImageNet.The results confirm the effectiveness of our framework.On CIFAR-100,our method reaches 55%accuracy with 37 fewer rounds and achieves a competitive final accuracy of 65.46%.Even under extreme non-IID scenarios,it delivers significant improvements in both accuracy and communication efficiency.The implementation is publicly available at https://github.com/sjmp525/CollaborativeComputing/tree/FedCCM(accessed on 20 October 2025).展开更多
With the rapid advancements in edge computing and artificial intelligence,federated learning(FL)has gained momentum as a promising approach to collaborative data utilization across organizations and devices,while ensu...With the rapid advancements in edge computing and artificial intelligence,federated learning(FL)has gained momentum as a promising approach to collaborative data utilization across organizations and devices,while ensuring data privacy and information security.In order to further harness the energy efficiency of wireless networks,an integrated sensing,communication and computation(ISCC)framework has been proposed,which is anticipated to be a key enabler in the era of 6G networks.Although the advantages of pushing intelligence to edge devices are multi-fold,some challenges arise when incorporating FL into wireless networks under the umbrella of ISCC.This paper provides a comprehensive survey of FL,with special emphasis on the design and optimization of ISCC.We commence by introducing the background and fundamentals of FL and the ISCC framework.Subsequently,the aforementioned challenges are highlighted and the state of the art in potential solutions is reviewed.Finally,design guidelines are provided for the incorporation of FL and ISCC.Overall,this paper aims to contribute to the understanding of FL in the context of wireless networks,with a focus on the ISCC framework,and provide insights into addressing the challenges and optimizing the design for the integration of FL into future 6G networks.展开更多
The convergence of computation and communication at network edges plays a significant role in coping with computation-intensive and delay-critical tasks.During the stage of network planning,the resource provisioning p...The convergence of computation and communication at network edges plays a significant role in coping with computation-intensive and delay-critical tasks.During the stage of network planning,the resource provisioning problem for edge nodes has to be investigated to provide prior information for future system configurations.This work focuses on how to quantify the computation capabilities of access points at network edges when provisioning resources of computation and communication in multi-cell wireless networks.The problem is formulated as a discrete and non-convex minimization problem,where practical constraints including delay requirements,the inter-cell interference,and resource allocation strategies are considered.An iterative algorithm is also developed based on decomposition theory and fractional programming to solve this problem.The analysis shows that the necessary computation capability needed for certain delay guarantee depends on resource allocation strategies for delay-critical tasks.For delay-tolerant tasks,it can be approximately estimated by a derived lower bound which ignores the scheduling strategy.The efficiency of the proposed algorithm is demonstrated using numerical results.展开更多
Benefit from the enhanced onboard processing capacities and high-speed satellite-terrestrial links,satellite edge computing has been regarded as a promising technique to facilitate the execution of the computation-int...Benefit from the enhanced onboard processing capacities and high-speed satellite-terrestrial links,satellite edge computing has been regarded as a promising technique to facilitate the execution of the computation-intensive applications for satellite communication networks(SCNs).By deploying edge computing servers in satellite and gateway stations,SCNs can achieve significant performance gains of the computing capacities at the expense of extending the dimensions and complexity of resource management.Therefore,in this paper,we investigate the joint computing and communication resource management problem for SCNs to minimize the execution latency of the computation-intensive applications,while two different satellite edge computing scenarios and local execution are considered.Furthermore,the joint computing and communication resource allocation problem for the computation-intensive services is formulated as a mixed-integer programming problem.A game-theoretic and many-to-one matching theorybased scheme(JCCRA-GM)is proposed to achieve an approximate optimal solution.Numerical results show that the proposed method with low complexity can achieve almost the same weight-sum latency as the Brute-force method.展开更多
This paper considers a UAV communication system with mobile edge computing(MEC).We minimize the energy consumption of the whole system via jointly optimizing the UAV's trajectory and task assignment as well as CPU...This paper considers a UAV communication system with mobile edge computing(MEC).We minimize the energy consumption of the whole system via jointly optimizing the UAV's trajectory and task assignment as well as CPU's computational speed under the set of resource constrains.To this end,we first derive the energy consumption model of data processing,and then obtain the energy consumption model of fixed-wing UAV's flight.The optimization problem is mathematically formulated.To address the problem,we first obtain the approximate optimization problem by applying the technique of discrete linear state-space approximation,and then transform the non-convex constraints into convex by using linearization.Furthermore,a concave-convex procedure(CCCP) based algorithm is proposed in order to solve the optimization problem approximately.Numerical results show the efficacy of the proposed algorithm.展开更多
The rapid expansion of railways,especially High-Speed Railways(HSRs),has drawn considerable interest from both academic and industrial sectors.To meet the future vision of smart rail communications,the rail transport ...The rapid expansion of railways,especially High-Speed Railways(HSRs),has drawn considerable interest from both academic and industrial sectors.To meet the future vision of smart rail communications,the rail transport industry must innovate in key technologies to ensure high-quality transmissions for passengers and railway operations.These systems must function effectively under high mobility conditions while prioritizing safety,ecofriendliness,comfort,transparency,predictability,and reliability.On the other hand,the proposal of 6 G wireless technology introduces new possibilities for innovation in communication technologies,which may truly realize the current vision of HSR.Therefore,this article gives a review of the current advanced 6 G wireless communication technologies for HSR,including random access and switching,channel estimation and beamforming,integrated sensing and communication,and edge computing.The main application scenarios of these technologies are reviewed,as well as their current research status and challenges,followed by an outlook on future development directions.展开更多
With the increasing maritime activities and the rapidly developing maritime economy, the fifth-generation(5G) mobile communication system is expected to be deployed at the ocean. New technologies need to be explored t...With the increasing maritime activities and the rapidly developing maritime economy, the fifth-generation(5G) mobile communication system is expected to be deployed at the ocean. New technologies need to be explored to meet the requirements of ultra-reliable and low latency communications(URLLC) in the maritime communication network(MCN). Mobile edge computing(MEC) can achieve high energy efficiency in MCN at the cost of suffering from high control plane latency and low reliability. In terms of this issue, the mobile edge communications, computing, and caching(MEC3) technology is proposed to sink mobile computing, network control, and storage to the edge of the network. New methods that enable resource-efficient configurations and reduce redundant data transmissions can enable the reliable implementation of computing-intension and latency-sensitive applications. The key technologies of MEC3 to enable URLLC are analyzed and optimized in MCN. The best response-based offloading algorithm(BROA) is adopted to optimize task offloading. The simulation results show that the task latency can be decreased by 26.5’ ms, and the energy consumption in terminal users can be reduced to 66.6%.展开更多
Multiple quantum well(MQW) Ⅲ-nitride diodes that can simultaneously emit and detect light feature an overlapping region between their electroluminescence and responsivity spectra, which allows them to be simultaneous...Multiple quantum well(MQW) Ⅲ-nitride diodes that can simultaneously emit and detect light feature an overlapping region between their electroluminescence and responsivity spectra, which allows them to be simultaneously used as both a transmitter and a receiver in a wireless light communication system. Here, we demonstrate a mobile light communication system using a time-division multiplexing(TDM) scheme to achieve bidirectional data transmission via the same optical channel.Two identical blue MQW diodes are defined by software as a transmitter or a receiver. To address the light alignment issue, an image identification module integrated with a gimbal stabilizer is used to automatically detect the locations of moving targets;thus, underwater audio communication is realized via a mobile blue-light TDM communication mode. This approach not only uses a single link but also integrates mobile nodes in a practical network.展开更多
In recent years,the exponential proliferation of smart devices with their intelligent applications poses severe challenges on conventional cellular networks.Such challenges can be potentially overcome by integrating c...In recent years,the exponential proliferation of smart devices with their intelligent applications poses severe challenges on conventional cellular networks.Such challenges can be potentially overcome by integrating communication,computing,caching,and control(i4C)technologies.In this survey,we first give a snapshot of different aspects of the i4C,comprising background,motivation,leading technological enablers,potential applications,and use cases.Next,we describe different models of communication,computing,caching,and control(4C)to lay the foundation of the integration approach.We review current stateof-the-art research efforts related to the i4C,focusing on recent trends of both conventional and artificial intelligence(AI)-based integration approaches.We also highlight the need for intelligence in resources integration.Then,we discuss the integration of sensing and communication(ISAC)and classify the integration approaches into various classes.Finally,we propose open challenges and present future research directions for beyond 5G networks,such as 6G.展开更多
5G is a new generation of mobile networking that aims to achieve unparalleled speed and performance. To accomplish this, three technologies, Device-to-Device communication (D2D), multi-access edge computing (MEC) and ...5G is a new generation of mobile networking that aims to achieve unparalleled speed and performance. To accomplish this, three technologies, Device-to-Device communication (D2D), multi-access edge computing (MEC) and network function virtualization (NFV) with ClickOS, have been a significant part of 5G, and this paper mainly discusses them. D2D enables direct communication between devices without the relay of base station. In 5G, a two-tier cellular network composed of traditional cellular network system and D2D is an efficient method for realizing high-speed communication. MEC unloads work from end devices and clouds platforms to widespread nodes, and connects the nodes together with outside devices and third-party providers, in order to diminish the overloading effect on any device caused by enormous applications and improve users’ quality of experience (QoE). There is also a NFV method in order to fulfill the 5G requirements. In this part, an optimized virtual machine for middle-boxes named ClickOS is introduced, and it is evaluated in several aspects. Some middle boxes are being implemented in the ClickOS and proved to have outstanding performances.展开更多
The centralized radio access cellular network infrastructure based on centralized Super Base Station(CSBS) is a promising solution to reduce the high construction cost and energy consumption of conventional cellular n...The centralized radio access cellular network infrastructure based on centralized Super Base Station(CSBS) is a promising solution to reduce the high construction cost and energy consumption of conventional cellular networks. With CSBS, the computing resource for communication protocol processing could be managed flexibly according the protocol load to improve the resource efficiency. Since the protocol load changes frequently and may exceed the capacity of processors, load balancing is needed. However, existing load balancing mechanisms used in data centers cannot satisfy the real-time requirement of the communication protocol processing. Therefore, a new computing resource adjustment scheme is proposed for communication protocol processing in the CSBS architecture. First of all, the main principles of protocol processing resource adjustment is concluded, followed by the analysis on the processing resource outage probability that the computing resource becomes inadequate for protocol processing as load changes. Following the adjustment principles, the proposed scheme is designed to reduce the processing resource outage probability based onthe optimized connected graph which is constructed by the approximate Kruskal algorithm. Simulation re-sults show that compared with the conventional load balancing mechanisms, the proposed scheme can reduce the occurrence number of inadequate processing resource and the additional resource consumption of adjustment greatly.展开更多
In the current cloud-based Internet-of-Things （IoT） model, smart devices （such as sensors, smartphones） exchange information through the Internet to cooperate and provide services to users, which could be citizens...In the current cloud-based Internet-of-Things （IoT） model, smart devices （such as sensors, smartphones） exchange information through the Internet to cooperate and provide services to users, which could be citizens, smart home systems, and industrial applications.展开更多
With the acceleration of the intelligent transformation of power systems,the requirements for communication technology are increasingly stringent.The application of 5G mobile communication technology in power communic...With the acceleration of the intelligent transformation of power systems,the requirements for communication technology are increasingly stringent.The application of 5G mobile communication technology in power communication is analyzed.In this study,5G technology features,application principles,and practical strategies are discussed,and methods such as network slicing,customized deployment,edge computing collaborative application,communication equipment integration and upgrading,and multi-technology collaboration and complementation are proposed.It aims to effectively improve the efficiency,reliability,and security of power communication,solve the problem that traditional communication technology is difficult to meet the diversified needs of power business,and achieve the effect of optimizing the power communication network and supporting the intelligent development of the power system.展开更多
To meet the intelligent detection needs of underwater defects in large hydropower stations,the hydrodynamic performance of a bionic streamlined remotely operated vehicle containing a thruster protective net structure ...To meet the intelligent detection needs of underwater defects in large hydropower stations,the hydrodynamic performance of a bionic streamlined remotely operated vehicle containing a thruster protective net structure is numerically simulated via computational fluid dynamics and overlapping mesh technology.The results show that the entity model generates greater hydrodynamic force during steady motion,whereas the square net model experiences greater force and moment during unsteady motion.The lateral and vertical force coefficients of the entity model are 4.32 and 3.13 times greater than those of the square net model in the oblique towing test simulation.The square net model also offers better static and dynamic stability,with a 24.5%increase in dynamic stability,achieving the highest lift-to-drag ratio at attack angles of 6°∼8°.This research provides valuable insights for designing and controlling underwater defect detection vehicles for large hydropower stations.展开更多
In the rapidly evolving landscape of Industry 4.0(I4.0),the convergence of information and operational technologies necessitates real-time communication and collaboration across cyber-physical systems and the Internet...In the rapidly evolving landscape of Industry 4.0(I4.0),the convergence of information and operational technologies necessitates real-time communication and collaboration across cyber-physical systems and the Internet of Things(IoT).Rapid data transmission is particularly critical within enterprises(vertically)and among stakeholders(horizontally)in this complex,heterogeneous ecosystem.While current research has focused on data application,processing,and storage within the cloud-edge-device continuum,cross-edge transmission has received less attention,resulting in challenges such as suboptimal routing and excessive delays in horizontal communications.To address the above issues,this paper introduces a Connection-As-Required Scheme(CARS)specifically designed for delay-sensitive IoT and Cyber-Physical System(CPS)applications,where low-latency communication is essential for operational efficiency.CARS leverages Lyapunov optimization and backpressure algorithms to optimize traffic scheduling and routing,minimizing communication delay between entities.Benchmarking against state-of-the-art solutions,CARS reduces Round-Trip Time(RTT)to approximately 47.0%of conventional methods and decreases delay by 24.5%in TCP-based and 26.0%in UDP-based applications.These results highlight the potential of CARS to facilitate effective,low-latency collaboration in diverse I4.0 environments.展开更多
Due to the high flexibility of Unmanned Aerial Vehicles(UAVs),equipping Mobile Edge Computing(MEC)servers on UAVs can effectively and rapidly handle the high computing requirements of computation-intensive tasks.Howev...Due to the high flexibility of Unmanned Aerial Vehicles(UAVs),equipping Mobile Edge Computing(MEC)servers on UAVs can effectively and rapidly handle the high computing requirements of computation-intensive tasks.However,the Line-of-Sight(LoS)transmission between the UAV and ground users makes the offloading information be easily monitored.Therefore,this paper proposes a covert communication scheme against a flying warden in UAV-assisted MEC system.In the proposed scheme,the UAV server assists ground users in completing the computation of offloading tasks.To reduce the possibility of the flying warden detecting the transmission behavior of ground users to the UAV server,a ground jamming device sends jamming signals to the flying warden.The minimum computing capacity of the system is maximized by jointly optimizing ground users’resources and the UAV server’s trajectory under the constraint of system covertness.Due to the multivariable coupling,the optimization problem is non-convex.The optimization problem is first transformed into a tractable form,and then the optimizing solution is iteratively obtained using Successive Convex Approximation(SCA)and Block Coordinate Descent(BCD)algorithms.Numerical results show that,compared to the benchmark schemes,the proposed scheme effectively enhances the computing capacity of the system while meeting the system’s covertness requirements.展开更多
In this paper, according to the AR4JA codes in deep space communication, two kinds of iterative decoding including partly parallel decoding and overlapped partly parallel decoding are analyzed, and the advantages and ...In this paper, according to the AR4JA codes in deep space communication, two kinds of iterative decoding including partly parallel decoding and overlapped partly parallel decoding are analyzed, and the advantages and disadvantages of them are listed. A modified overlapped partly parallel decoding that not only inherits the advantages of the two algorithms, but also overcomes the shortcomings of the two algorithms is proposed. The simulation results show that the three kinds of decoding have the same decoding performance; modified overlapped partly parallel decoding improves the iterative convergence rate and the throughput of system.展开更多
基金supported by Ongoing Research Funding Program(ORF-2025-488)King Saud University,Riyadh,Saudi Arabia.
文摘Effectively handling imbalanced datasets remains a fundamental challenge in computational modeling and machine learning,particularly when class overlap significantly deteriorates classification performance.Traditional oversampling methods often generate synthetic samples without considering density variations,leading to redundant or misleading instances that exacerbate class overlap in high-density regions.To address these limitations,we propose Wasserstein Generative Adversarial Network Variational Density Estimation WGAN-VDE,a computationally efficient density-aware adversarial resampling framework that enhances minority class representation while strategically reducing class overlap.The originality of WGAN-VDE lies in its density-aware sample refinement,ensuring that synthetic samples are positioned in underrepresented regions,thereby improving class distinctiveness.By applying structured feature representation,targeted sample generation,and density-based selection mechanisms strategies,the proposed framework ensures the generation of well-separated and diverse synthetic samples,improving class separability and reducing redundancy.The experimental evaluation on 20 benchmark datasets demonstrates that this approach outperforms 11 state-of-the-art rebalancing techniques,achieving superior results in F1-score,Accuracy,G-Mean,and AUC metrics.These results establish the proposed method as an effective and robust computational approach,suitable for diverse engineering and scientific applications involving imbalanced data classification and computational modeling.
基金funded by the deanship of scientific research(DSR),King Abdukaziz University,Jeddah,under grant No.(G-1436-611-225)。
文摘The rapid evolution of wireless technologies and the advent of 6G networks present new challenges and opportunities for Internet ofThings(IoT)applications,particularly in terms of ultra-reliable,secure,and energyefficient communication.This study explores the integration of Reconfigurable Intelligent Surfaces(RIS)into IoT networks to enhance communication performance.Unlike traditional passive reflector-based approaches,RIS is leveraged as an active optimization tool to improve both backscatter and direct communication modes,addressing critical IoT challenges such as energy efficiency,limited communication range,and double-fading effects in backscatter communication.We propose a novel computational framework that combines RIS functionality with Physical Layer Security(PLS)mechanisms,optimized through the algorithm known as Deep Deterministic Policy Gradient(DDPG).This framework adaptively adapts RIS configurations and transmitter beamforming to reduce key challenges,including imperfect channel state information(CSI)and hardware limitations like quantized RIS phase shifts.By optimizing both RIS settings and beamforming in real-time,our approach outperforms traditional methods by significantly increasing secrecy rates,improving spectral efficiency,and enhancing energy efficiency.Notably,this framework adapts more effectively to the dynamic nature of wireless channels compared to conventional optimization techniques,providing scalable solutions for large-scale RIS deployments.Our results demonstrate substantial improvements in communication performance setting a new benchmark for secure,efficient and scalable 6G communication.This work offers valuable insights for the future of IoT networks,with a focus on computational optimization,high spectral efficiency and energy-aware operations.
基金supported by the National Natural Science Foundation of China(62202215)Liaoning Province Applied Basic Research Program(Youth Special Project,2023JH2/101600038)+4 种基金Shenyang Youth Science and Technology Innovation Talent Support Program(RC220458)Guangxuan Program of Shenyang Ligong University(SYLUGXRC202216)the Basic Research Special Funds for Undergraduate Universities in Liaoning Province(LJ212410144067)the Natural Science Foundation of Liaoning Province(2024-MS-113)the science and technology funds from Liaoning Education Department(LJKZ0242).
文摘In the field of edge computing,achieving low-latency computational task offloading with limited resources is a critical research challenge,particularly in resource-constrained and latency-sensitive vehicular network environments where rapid response is mandatory for safety-critical applications.In scenarios where edge servers are sparsely deployed,the lack of coordination and information sharing often leads to load imbalance,thereby increasing system latency.Furthermore,in regions without edge server coverage,tasks must be processed locally,which further exacerbates latency issues.To address these challenges,we propose a novel and efficient Deep Reinforcement Learning(DRL)-based approach aimed at minimizing average task latency.The proposed method incorporates three offloading strategies:local computation,direct offloading to the edge server in local region,and device-to-device(D2D)-assisted offloading to edge servers in other regions.We formulate the task offloading process as a complex latency minimization optimization problem.To solve it,we propose an advanced algorithm based on the Dueling Double Deep Q-Network(D3QN)architecture and incorporating the Prioritized Experience Replay(PER)mechanism.Experimental results demonstrate that,compared with existing offloading algorithms,the proposed method significantly reduces average task latency,enhances user experience,and offers an effective strategy for latency optimization in future edge computing systems under dynamic workloads.
基金supported by the National Natural Science Foundation of China(62462040)the Yunnan Fundamental Research Projects(202501AT070345)the Major Science and Technology Projects in Yunnan Province(202202AD080013).
文摘Federated learning often experiences slow and unstable convergence due to edge-side data heterogeneity.This problem becomes more severe when edge participation rate is low,as the information collected from different edge devices varies significantly.As a result,communication overhead increases,which further slows down the convergence process.To address this challenge,we propose a simple yet effective federated learning framework that improves consistency among edge devices.The core idea is clusters the lookahead gradients collected from edge devices on the cloud server to obtain personalized momentum for steering local updates.In parallel,a global momentum is applied during model aggregation,enabling faster convergence while preserving personalization.This strategy enables efficient propagation of the estimated global update direction to all participating edge devices and maintains alignment in local training,without introducing extra memory or communication overhead.We conduct extensive experiments on benchmark datasets such as Cifar100 and Tiny-ImageNet.The results confirm the effectiveness of our framework.On CIFAR-100,our method reaches 55%accuracy with 37 fewer rounds and achieves a competitive final accuracy of 65.46%.Even under extreme non-IID scenarios,it delivers significant improvements in both accuracy and communication efficiency.The implementation is publicly available at https://github.com/sjmp525/CollaborativeComputing/tree/FedCCM(accessed on 20 October 2025).
文摘With the rapid advancements in edge computing and artificial intelligence,federated learning(FL)has gained momentum as a promising approach to collaborative data utilization across organizations and devices,while ensuring data privacy and information security.In order to further harness the energy efficiency of wireless networks,an integrated sensing,communication and computation(ISCC)framework has been proposed,which is anticipated to be a key enabler in the era of 6G networks.Although the advantages of pushing intelligence to edge devices are multi-fold,some challenges arise when incorporating FL into wireless networks under the umbrella of ISCC.This paper provides a comprehensive survey of FL,with special emphasis on the design and optimization of ISCC.We commence by introducing the background and fundamentals of FL and the ISCC framework.Subsequently,the aforementioned challenges are highlighted and the state of the art in potential solutions is reviewed.Finally,design guidelines are provided for the incorporation of FL and ISCC.Overall,this paper aims to contribute to the understanding of FL in the context of wireless networks,with a focus on the ISCC framework,and provide insights into addressing the challenges and optimizing the design for the integration of FL into future 6G networks.
基金Supported by the Shanghai Sailing Program(No.18YF1427900)the National Natural Science Foundation of China(No.61471347)the Shanghai Pujiang Program(No.2020PJD081).
文摘The convergence of computation and communication at network edges plays a significant role in coping with computation-intensive and delay-critical tasks.During the stage of network planning,the resource provisioning problem for edge nodes has to be investigated to provide prior information for future system configurations.This work focuses on how to quantify the computation capabilities of access points at network edges when provisioning resources of computation and communication in multi-cell wireless networks.The problem is formulated as a discrete and non-convex minimization problem,where practical constraints including delay requirements,the inter-cell interference,and resource allocation strategies are considered.An iterative algorithm is also developed based on decomposition theory and fractional programming to solve this problem.The analysis shows that the necessary computation capability needed for certain delay guarantee depends on resource allocation strategies for delay-critical tasks.For delay-tolerant tasks,it can be approximately estimated by a derived lower bound which ignores the scheduling strategy.The efficiency of the proposed algorithm is demonstrated using numerical results.
基金This work was supported by the National Natural Science Foundation of China(Grants 61971054 and 61601045)Science and Technology on Information Transmission and Dissemination in Communication Networks Laboratory Foundation(HHX21641X002 and HHX20641X003).
文摘Benefit from the enhanced onboard processing capacities and high-speed satellite-terrestrial links,satellite edge computing has been regarded as a promising technique to facilitate the execution of the computation-intensive applications for satellite communication networks(SCNs).By deploying edge computing servers in satellite and gateway stations,SCNs can achieve significant performance gains of the computing capacities at the expense of extending the dimensions and complexity of resource management.Therefore,in this paper,we investigate the joint computing and communication resource management problem for SCNs to minimize the execution latency of the computation-intensive applications,while two different satellite edge computing scenarios and local execution are considered.Furthermore,the joint computing and communication resource allocation problem for the computation-intensive services is formulated as a mixed-integer programming problem.A game-theoretic and many-to-one matching theorybased scheme(JCCRA-GM)is proposed to achieve an approximate optimal solution.Numerical results show that the proposed method with low complexity can achieve almost the same weight-sum latency as the Brute-force method.
基金supported in part by National Natural Science Foundation of China(Grant No.61702149,U1709220)
文摘This paper considers a UAV communication system with mobile edge computing(MEC).We minimize the energy consumption of the whole system via jointly optimizing the UAV's trajectory and task assignment as well as CPU's computational speed under the set of resource constrains.To this end,we first derive the energy consumption model of data processing,and then obtain the energy consumption model of fixed-wing UAV's flight.The optimization problem is mathematically formulated.To address the problem,we first obtain the approximate optimization problem by applying the technique of discrete linear state-space approximation,and then transform the non-convex constraints into convex by using linearization.Furthermore,a concave-convex procedure(CCCP) based algorithm is proposed in order to solve the optimization problem approximately.Numerical results show the efficacy of the proposed algorithm.
基金National Natural Science Foundation of China(U2468201,62122012,62221001).
文摘The rapid expansion of railways,especially High-Speed Railways(HSRs),has drawn considerable interest from both academic and industrial sectors.To meet the future vision of smart rail communications,the rail transport industry must innovate in key technologies to ensure high-quality transmissions for passengers and railway operations.These systems must function effectively under high mobility conditions while prioritizing safety,ecofriendliness,comfort,transparency,predictability,and reliability.On the other hand,the proposal of 6 G wireless technology introduces new possibilities for innovation in communication technologies,which may truly realize the current vision of HSR.Therefore,this article gives a review of the current advanced 6 G wireless communication technologies for HSR,including random access and switching,channel estimation and beamforming,integrated sensing and communication,and edge computing.The main application scenarios of these technologies are reviewed,as well as their current research status and challenges,followed by an outlook on future development directions.
基金the National S&T Major Project (No. 2018ZX03001011)the National Key R&D Program(No.2018YFB1801102)+1 种基金the National Natural Science Foundation of China (No. 61671072)the Beijing Natural Science Foundation (No. L192025)
文摘With the increasing maritime activities and the rapidly developing maritime economy, the fifth-generation(5G) mobile communication system is expected to be deployed at the ocean. New technologies need to be explored to meet the requirements of ultra-reliable and low latency communications(URLLC) in the maritime communication network(MCN). Mobile edge computing(MEC) can achieve high energy efficiency in MCN at the cost of suffering from high control plane latency and low reliability. In terms of this issue, the mobile edge communications, computing, and caching(MEC3) technology is proposed to sink mobile computing, network control, and storage to the edge of the network. New methods that enable resource-efficient configurations and reduce redundant data transmissions can enable the reliable implementation of computing-intension and latency-sensitive applications. The key technologies of MEC3 to enable URLLC are analyzed and optimized in MCN. The best response-based offloading algorithm(BROA) is adopted to optimize task offloading. The simulation results show that the task latency can be decreased by 26.5’ ms, and the energy consumption in terminal users can be reduced to 66.6%.
基金jointly supported by the National Natural Science Foundation of China (U21A20495)Natural Science Foundation of Jiangsu Province (BG2024023)+1 种基金National Key Research and Development Program of China (2022YFE0112000)111 Project (D17018)。
文摘Multiple quantum well(MQW) Ⅲ-nitride diodes that can simultaneously emit and detect light feature an overlapping region between their electroluminescence and responsivity spectra, which allows them to be simultaneously used as both a transmitter and a receiver in a wireless light communication system. Here, we demonstrate a mobile light communication system using a time-division multiplexing(TDM) scheme to achieve bidirectional data transmission via the same optical channel.Two identical blue MQW diodes are defined by software as a transmitter or a receiver. To address the light alignment issue, an image identification module integrated with a gimbal stabilizer is used to automatically detect the locations of moving targets;thus, underwater audio communication is realized via a mobile blue-light TDM communication mode. This approach not only uses a single link but also integrates mobile nodes in a practical network.
基金supported in part by National Key R&D Program of China(2019YFE0196400)Key Research and Development Program of Shaanxi(2022KWZ09)+4 种基金National Natural Science Foundation of China(61771358,61901317,62071352)Fundamental Research Funds for the Central Universities(JB190104)Joint Education Project between China and Central-Eastern European Countries(202005)the 111 Project(B08038)。
文摘In recent years,the exponential proliferation of smart devices with their intelligent applications poses severe challenges on conventional cellular networks.Such challenges can be potentially overcome by integrating communication,computing,caching,and control(i4C)technologies.In this survey,we first give a snapshot of different aspects of the i4C,comprising background,motivation,leading technological enablers,potential applications,and use cases.Next,we describe different models of communication,computing,caching,and control(4C)to lay the foundation of the integration approach.We review current stateof-the-art research efforts related to the i4C,focusing on recent trends of both conventional and artificial intelligence(AI)-based integration approaches.We also highlight the need for intelligence in resources integration.Then,we discuss the integration of sensing and communication(ISAC)and classify the integration approaches into various classes.Finally,we propose open challenges and present future research directions for beyond 5G networks,such as 6G.
文摘5G is a new generation of mobile networking that aims to achieve unparalleled speed and performance. To accomplish this, three technologies, Device-to-Device communication (D2D), multi-access edge computing (MEC) and network function virtualization (NFV) with ClickOS, have been a significant part of 5G, and this paper mainly discusses them. D2D enables direct communication between devices without the relay of base station. In 5G, a two-tier cellular network composed of traditional cellular network system and D2D is an efficient method for realizing high-speed communication. MEC unloads work from end devices and clouds platforms to widespread nodes, and connects the nodes together with outside devices and third-party providers, in order to diminish the overloading effect on any device caused by enormous applications and improve users’ quality of experience (QoE). There is also a NFV method in order to fulfill the 5G requirements. In this part, an optimized virtual machine for middle-boxes named ClickOS is introduced, and it is evaluated in several aspects. Some middle boxes are being implemented in the ClickOS and proved to have outstanding performances.
基金supported in part by the National Science Foundationof China under Grant number 61431001the Beijing Talents Fund under Grant number 2015000021223ZK31
文摘The centralized radio access cellular network infrastructure based on centralized Super Base Station(CSBS) is a promising solution to reduce the high construction cost and energy consumption of conventional cellular networks. With CSBS, the computing resource for communication protocol processing could be managed flexibly according the protocol load to improve the resource efficiency. Since the protocol load changes frequently and may exceed the capacity of processors, load balancing is needed. However, existing load balancing mechanisms used in data centers cannot satisfy the real-time requirement of the communication protocol processing. Therefore, a new computing resource adjustment scheme is proposed for communication protocol processing in the CSBS architecture. First of all, the main principles of protocol processing resource adjustment is concluded, followed by the analysis on the processing resource outage probability that the computing resource becomes inadequate for protocol processing as load changes. Following the adjustment principles, the proposed scheme is designed to reduce the processing resource outage probability based onthe optimized connected graph which is constructed by the approximate Kruskal algorithm. Simulation re-sults show that compared with the conventional load balancing mechanisms, the proposed scheme can reduce the occurrence number of inadequate processing resource and the additional resource consumption of adjustment greatly.
文摘In the current cloud-based Internet-of-Things （IoT） model, smart devices （such as sensors, smartphones） exchange information through the Internet to cooperate and provide services to users, which could be citizens, smart home systems, and industrial applications.
文摘With the acceleration of the intelligent transformation of power systems,the requirements for communication technology are increasingly stringent.The application of 5G mobile communication technology in power communication is analyzed.In this study,5G technology features,application principles,and practical strategies are discussed,and methods such as network slicing,customized deployment,edge computing collaborative application,communication equipment integration and upgrading,and multi-technology collaboration and complementation are proposed.It aims to effectively improve the efficiency,reliability,and security of power communication,solve the problem that traditional communication technology is difficult to meet the diversified needs of power business,and achieve the effect of optimizing the power communication network and supporting the intelligent development of the power system.
基金supported by the National Key R&D Program of China(Grant No.2022YFB4703401).
文摘To meet the intelligent detection needs of underwater defects in large hydropower stations,the hydrodynamic performance of a bionic streamlined remotely operated vehicle containing a thruster protective net structure is numerically simulated via computational fluid dynamics and overlapping mesh technology.The results show that the entity model generates greater hydrodynamic force during steady motion,whereas the square net model experiences greater force and moment during unsteady motion.The lateral and vertical force coefficients of the entity model are 4.32 and 3.13 times greater than those of the square net model in the oblique towing test simulation.The square net model also offers better static and dynamic stability,with a 24.5%increase in dynamic stability,achieving the highest lift-to-drag ratio at attack angles of 6°∼8°.This research provides valuable insights for designing and controlling underwater defect detection vehicles for large hydropower stations.
基金supported by the National Natural Science Foundation of China under Grant No.62271040,and 62341102the Fundamental Research Funds for the Central Universities under Grant No.2023JBGP004.
文摘In the rapidly evolving landscape of Industry 4.0(I4.0),the convergence of information and operational technologies necessitates real-time communication and collaboration across cyber-physical systems and the Internet of Things(IoT).Rapid data transmission is particularly critical within enterprises(vertically)and among stakeholders(horizontally)in this complex,heterogeneous ecosystem.While current research has focused on data application,processing,and storage within the cloud-edge-device continuum,cross-edge transmission has received less attention,resulting in challenges such as suboptimal routing and excessive delays in horizontal communications.To address the above issues,this paper introduces a Connection-As-Required Scheme(CARS)specifically designed for delay-sensitive IoT and Cyber-Physical System(CPS)applications,where low-latency communication is essential for operational efficiency.CARS leverages Lyapunov optimization and backpressure algorithms to optimize traffic scheduling and routing,minimizing communication delay between entities.Benchmarking against state-of-the-art solutions,CARS reduces Round-Trip Time(RTT)to approximately 47.0%of conventional methods and decreases delay by 24.5%in TCP-based and 26.0%in UDP-based applications.These results highlight the potential of CARS to facilitate effective,low-latency collaboration in diverse I4.0 environments.
基金supported in part by the Zhejiang Provincial Natural Science Foundation of China(No.LR25F010003)in part by the National Natural Science Foundation of China(Nos.62271447,61871348 and 62471090)+1 种基金in part by the Natural Science Foundation of Sichuan Province of China(No.2023NSFSC047)in part by the Fundamental Research Funds for the Provincial Universities of Zhejiang,China(No.RF-C2023008).
文摘Due to the high flexibility of Unmanned Aerial Vehicles(UAVs),equipping Mobile Edge Computing(MEC)servers on UAVs can effectively and rapidly handle the high computing requirements of computation-intensive tasks.However,the Line-of-Sight(LoS)transmission between the UAV and ground users makes the offloading information be easily monitored.Therefore,this paper proposes a covert communication scheme against a flying warden in UAV-assisted MEC system.In the proposed scheme,the UAV server assists ground users in completing the computation of offloading tasks.To reduce the possibility of the flying warden detecting the transmission behavior of ground users to the UAV server,a ground jamming device sends jamming signals to the flying warden.The minimum computing capacity of the system is maximized by jointly optimizing ground users’resources and the UAV server’s trajectory under the constraint of system covertness.Due to the multivariable coupling,the optimization problem is non-convex.The optimization problem is first transformed into a tractable form,and then the optimizing solution is iteratively obtained using Successive Convex Approximation(SCA)and Block Coordinate Descent(BCD)algorithms.Numerical results show that,compared to the benchmark schemes,the proposed scheme effectively enhances the computing capacity of the system while meeting the system’s covertness requirements.
基金Sponsored by the National Natural Science Foundation of China( Grant No. 61032003)the Fundamental Research Funds for the Central Universities( Grant No. HIT. NSRIF.2012021)
文摘In this paper, according to the AR4JA codes in deep space communication, two kinds of iterative decoding including partly parallel decoding and overlapped partly parallel decoding are analyzed, and the advantages and disadvantages of them are listed. A modified overlapped partly parallel decoding that not only inherits the advantages of the two algorithms, but also overcomes the shortcomings of the two algorithms is proposed. The simulation results show that the three kinds of decoding have the same decoding performance; modified overlapped partly parallel decoding improves the iterative convergence rate and the throughput of system.
