期刊文献+
任意字段
题名或关键词
题名
关键词
摘要
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息
共找到672篇文章
< 1 2 … 34 >
每页显示 20 50 100
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
显示方式：
Secure and robust resource allocation for UAV-assisted anti-jamming spectrum sharing under uncertain jamming in airborne maneuvering networks 认领 引用
1
作者 Rui DING Haotian QUAN +1 位作者 Zhijie ZENG Fuhui ZHOU 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第5期111-121,共11页
Airborne Maneuvering Network(AMN)has attracted great attention in diverse practical scenarios.Low-altitude maneuvering UAV plays as the operational backbone of AMN to promote the development of an efficient,secure,and... Airborne Maneuvering Network(AMN)has attracted great attention in diverse practical scenarios.Low-altitude maneuvering UAV plays as the operational backbone of AMN to promote the development of an efficient,secure,and low-latency AMN by providing new airborne wireless nerve tracts.However,the widespread adoption of AMNs witnesses a drastic increase of mobile users and data-intensive applications,which makes it suffer from the intense spectrum competition.Spectrum sharing shows promise in alleviating the severe spectrum scarcity of AMNs.Moreover,due to the broadcast nature of wireless channels and the increasing probability of the line-of-sight transmission,the AMN is vulnerable to malicious jamming,especially encountering the coupled uncertainty and dynamic jamming.To solve these problems,UAV-assisted antijamming spectrum sharing in AMNs is investigated.We propose a joint space-power-frequency domain optimization approach to simultaneously mitigate both external malicious jamming and internal sharing interference.The sum rate maximization of the secondary network is studied by jointly optimizing the UAV transmit power,sub-band allocation,and trajectory.To tackle the formulated intractable non-convex problem,we propose a computationally efficient iterative algorithm based on alternating optimization,integrating the S-procedure to handle bounded uncertainties and the successive convex approximation to obtain near-optimal convex solutions.Extensive simulation results show that our proposed scheme can significantly increase the sum transmission rate.Moreover,it is shown that our proposed scheme is the best robust among all benchmark schemes against jammer location and power uncertainties,confirming the practicality for the next-generation UAV based airborne networks. 展开更多
关键词 Airborne maneuvering networks Anti-jamming Resource allocation Spectrum management UAV communication
暂未订购 下载PDF
Heterogeneous resource allocation with latency guarantee for computing power network 认领 引用
2
作者 Ailing Zhong Dapeng Wu +1 位作者 Boran Yang Ruyan Wang 《Digital Communications and Networks》 SCIE EI CSCD 2026年第1期25-37,共13页
Computing Power Network(CPN)is a new paradigm that integrates communication,computing,and storage resources to provide services for tasks.However,tasks composed of non-independent subtasks have a preference for the re... Computing Power Network(CPN)is a new paradigm that integrates communication,computing,and storage resources to provide services for tasks.However,tasks composed of non-independent subtasks have a preference for the resources required at each stage,which increases the difficulty of heterogeneous resource allocation and reduces the latency performance of CPN services.Motivated by this,this paper jointly optimizes the full-service cycle of tasks,including transmission,task partitioning,and offloading.First,the transmission bandwidth is dynamically configured based on delay sensitivity of tasks.Second,with the real-time information from edge resource clusters and state resource clusters in the network,the optimal partitioning for a computation task is derived.Third,personalized resource allocation schemes are customized for computation and storage tasks respectively.Finally,the impact of resource parameter configuration on the latency violation probability of CPN is revealed.Moreover,compared with the benchmark schemes,our proposed scheme reduces the network latency violation probability by up to 1.17×in the same network setting. 展开更多
关键词 Latency violation probability Subtask dependencies Resource allocation Computing power network
暂未订购 下载PDF
Impact of resource allocation on information-disease coupled propagation considering node importance in multiplex networks 认领 引用
3
作者 Liang’an Huo Jiaxue Cha Yue Yu 《Chinese Physics B》 SCIE EI CAS CSCD 2026年第7期901-913,共13页
During a large-scale epidemic outbreak,effective resource allocation is vital for controlling disease transmission.Prioritizing resource provision to communities experiencing severe infections can mitigate further spr... During a large-scale epidemic outbreak,effective resource allocation is vital for controlling disease transmission.Prioritizing resource provision to communities experiencing severe infections can mitigate further spread,while prioritizing information dissemination to influential individuals can expand the publicity effect.This paper proposes a novel two-layer information-disease transmission coupled model that optimizes the allocation of information and medical resources based on node importance analysis,aiming to explore the synergistic effects of resource allocation on disease dynamics.The study employs the microscopic Markov chain approach to construct dynamic equations and derive the epidemic threshold,with Monte Carlo simulations used to validate the theoretical results.Findings demonstrate that expanding the scope of preventive information dissemination through mass media improves public awareness of disease prevention and significantly curbs epidemic transmission.Moreover,reducing the resource deployment threshold in infected communities enables more precise resource allocation during the early stages of an outbreak,which is vital for increasing the epidemic threshold and reducing the final size of the epidemic.These findings provide robust theoretical foundations and actionable guidelines for optimizing resource allocation strategies in public health emergency management. 展开更多
关键词 information dissemination disease transmission resource allocation node importance multilayer network
暂未订购 下载PDF
Intra-Independent Distributed Resource Allocation Game 认领 引用 被引量：1
4
作者 Jialing Zhou Guanghui Wen +2 位作者 Yuezu Lv Tao Yang Guanrong Chen 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第10期2150-2152,共3页
Dear Editor,This letter deals with distributed resource allocation(DRA)over multiple interacting coalitions,where conflicts of interest may arise due to the relevance of one coalition’s decision to other coalitions’... Dear Editor,This letter deals with distributed resource allocation(DRA)over multiple interacting coalitions,where conflicts of interest may arise due to the relevance of one coalition’s decision to other coalitions’benefits.To address this challenge,a new model called intra-independent resource allocation game(IIRAG)is formulated under the framework of multi-coalition games.A new DRA algorithm is developed,which draws on techniques of variable replacement and leaderfollowing consensus.The proposed algorithm ensures linear convergence of the collective decision to the Nash equilibrium(NE)of the IIRAG,as well as satisfaction of the resource constraint throughout the iteration process.Numerical simulations validate the effectiveness of the proposed approach. 展开更多
关键词 distributed resource allocation multi coalition games variable replacement leader following consensus distributed resource allocation dra Nash equilibrium conflicts interest intra independent resource allocation game
暂未订购 下载PDF
A novel digital cousin based intelligent resource allocation scheme for secure low-altitude UAV spectrum sharing in AMN 认领 引用
5
作者 Zhijie ZENG Rui DING +3 位作者 Yuhang WU Yihe DIAO Fuhui ZHOU Qihui WU 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第5期122-131,共10页
The Airborne Maneuvering Network(AMN)is becoming an emerging field due to its wide-area coverage and localized service enhancement characteristics,in which Low-altitude Unmanned Aerial Vehicles(LUAVs)interact directly... The Airborne Maneuvering Network(AMN)is becoming an emerging field due to its wide-area coverage and localized service enhancement characteristics,in which Low-altitude Unmanned Aerial Vehicles(LUAVs)interact directly with ground-based devices after receiving commands from upper layers.However,the exponential increase in communication devices has led to a severe scarcity of spectrum for LUAVs.Furthermore,LUAVs communications are highly susceptible to interception by Eavesdroppers(Eves)due to the open characteristic of the wireless environment.Therefore,a secure spectrum sharing at LUAVs layer in AMN is studied.Moreover,to address the issue that the dynamic and heterogeneous characteristic of wireless environments presents significant challenges for resource allocation,a Digital Cousin based Q-learning(DCQ)method is proposed.Specifically,the original Probability Transition Matrix(PTM)obtained from sampling in the environment is transformed using the co-link method to obtain multiple virtual environments.Multiple agents are trained in parallel in multiple environments and the training results are fused to obtain the final Q function to output the policy of the original environment.The simulation results demonstrate that the proposed scheme can achieve more robust policies and faster convergence compared to conventional Deep Reinforcement Learning(DRL)methods. 展开更多
关键词 Digital cousin Eavesdropping LUAVs Spectrum sharing Resource allocation
暂未订购 下载PDF
Intelligent Resource Allocation for Multiaccess Edge Computing in 5G Ultra-Dense Slicing Network Using Federated Multiagent DDPG Algorithm 认领 引用
6
作者 Gong Yu Gong Pengwei +3 位作者 Jiang He Xie Wen Wang Chenxi Xu Peijun 《China Communications》 SCIE EI CSCD 2026年第1期273-289,共17页
Nowadays,advances in communication technology and cloud computing have spawned a variety of smart mobile devices,which will generate a great amount of computing-intensive businesses,and require corresponding resources... Nowadays,advances in communication technology and cloud computing have spawned a variety of smart mobile devices,which will generate a great amount of computing-intensive businesses,and require corresponding resources of computation and communication.Multiaccess edge computing(MEC)can offload computing-intensive tasks to the nearby edge servers,which alleviates the pressure of devices.Ultra-dense network(UDN)can provide effective spectrum resources by deploying a large number of micro base stations.Furthermore,network slicing can support various applications in different communication scenarios.Therefore,this paper integrates the ultra-dense network slicing and the MEC technology,and introduces a hybrid computing offloading strategy in order to satisfy various quality of service(QoS)of edge devices.In order to dynamically allocate limited resources,the above problem is formulated as multiagent distributed deep reinforcement learning(DRL),which will achieve low overhead computation offloading strategy and real-time resource allocation decisions.In this context,federated learning is added to train DRL agents in a distributed manner,where each agent is dedicated to exploring actions composed of offloading decisions and allocating resources,so as to jointly optimize system delay and energy consumption.Simulation results show that the proposed learning algorithm has better performance compared with other strategies in literature. 展开更多
关键词 federated learning multiaccess edge computing mutiagent deep reinforcement learning resource allocation ultra-dense slicing network
暂未订购 下载PDF
Energy-Efficient Network Deployment and Resource Allocation in IAB Aerial-Terrestrial Network 认领 引用
7
作者 Wang Wei Sheng Min +2 位作者 Chen Xuhui Liu Junyu Li Jiandong 《China Communications》 SCIE EI CSCD 2026年第3期330-347,共18页
This paper aims to improve energy efficiency(EE)of the integrated access and backhaul(IAB)aerial-terrestrial network,facilitating rapid and adjustable network infrastructure deployment.This is challenging,as interfere... This paper aims to improve energy efficiency(EE)of the integrated access and backhaul(IAB)aerial-terrestrial network,facilitating rapid and adjustable network infrastructure deployment.This is challenging,as interference generated by backhaul and access links degrades network throughput,and power imbalance between these links increases overall energy consumption.To this end,we jointly optimize aerial base station(ABS)deployment,user association,and downlink power allocation for both terrestrial base station and ABSs to maximize network EE.Specifically,using fractional programming,the EE maximization problem is transformed into a subtractive-form parametric problem,and then decomposed into ABS deployment and resource allocation subproblems.A hybrid algorithm combining particle swarm optimization and simulated annealing is proposed to solve the ABS deployment subproblem,determining ABS spatial configurations and updating power allocation given fixed user association.Meanwhile,a dynamic power allocation in response to network load is designed to solve the resource allocation subproblem.Furthermore,considering the quality of service requirements of ground users and the transmit power constraints of base stations,a joint EE optimization algorithm is proposed to enhance the network EE.Simulation results validate the effectiveness of the proposed methods in improving network EE,especially in scenarios involving more deployed ABSs. 展开更多
关键词 aerial-terrestrial network energy efficiency integrated access and backhaul(IAB) network deployment resource allocation
暂未订购 下载PDF
MWaOA:A Bio-Inspired Metaheuristic Algorithm for Resource Allocation in Internet of Things 认领 引用
8
作者 Rekha Phadke Abdul Lateef Haroon Phulara Shaik +3 位作者 Dayanidhi Mohapatra Doaa Sami Khafaga Eman Abdullah Aldakheel N.Sathyanarayana 《Computers, Materials & Continua》 SCIE EI 2026年第2期1285-1310,共26页
Recently,the Internet of Things(IoT)technology has been utilized in a wide range of services and applications which significantly transforms digital ecosystems through seamless interconnectivity between various smart ... Recently,the Internet of Things(IoT)technology has been utilized in a wide range of services and applications which significantly transforms digital ecosystems through seamless interconnectivity between various smart devices.Furthermore,the IoT plays a key role in multiple domains,including industrial automation,smart homes,and intelligent transportation systems.However,an increasing number of connected devices presents significant challenges related to efficient resource allocation and system responsiveness.To address these issue,this research proposes a Modified Walrus Optimization Algorithm(MWaOA)for effective resource management in smart IoT systems.In the proposed MWaOA,a crowding process is incorporated to maintain diversity and avoid premature convergence thereby enhancing the global search capability.During resource allocation,the MWaOA prevents early convergence,which aids in achieving a better balance between the exploration and exploitation phases during optimization.Empirical evaluations show that the MWaOA reduces energy consumption by approximately 4% to 34%and minimizes the response time by 6% to 33% across different service arrival rates.Compared to traditional optimization algorithms,MWaOA reduces energy consumption by 5% to 30%and minimizes the response time by 4% to 28% across different simulation epochs.The proposed MWaOA provides adaptive and robust resource allocation,thereby minimizing transmission cost while considering network constraints and real-time performance parameters. 展开更多
关键词 Delay gateway internet of things resource allocation resource management walrus optimization algorithm
暂未订购 下载PDF
A two-stage resource allocation algorithm for ISAC in the HRIS-aided FD network 认领 引用
9
作者 Pan Kunbei Wang Jiaming +2 位作者 Zhou Bin Huang Bo Huang Yanjun 《High Technology Letters》 EI CAS 2026年第1期21-29,共9页
Integrated sensing and communication(ISAC) systems can enhance security and reliability in full-duplex( FD) networks.This paper studies the resource allocation problem in FD networks based on ISAC.We formulate an opti... Integrated sensing and communication(ISAC) systems can enhance security and reliability in full-duplex( FD) networks.This paper studies the resource allocation problem in FD networks based on ISAC.We formulate an optimization problem that jointly considers beamforming,radar waveform,and reflection coefficient of a hybrid reconfigurable intelligent surface,aiming to maximize the beampattern gain of the concealed target while maintaining the quality of service of communication users.A two-stage algorithm is designed to solve this problem: the first stage optimizes the radar waveform and beamforming,and the second focuses on the reflection coefficient design.We acquire the feasible solution using semidefinite programming relaxation.The optimality of the feasible solution for radar waveform and beamforming subproblem is ensured through Cauchy-Schwartz inequality.For the reflection coefficient design,an approximate optimal solution is acquired through successive convex approximation.Numerical results demonstrate that the proposed algorithm can achieve a superior performance trade-off between communication and radar sensing compared to the benchmarks. 展开更多
关键词 integrated sensing and communication full-duplex hybrid reconfigurable intelligent surface resource allocation beampattern gain
暂未订购 下载PDF
Energy minimized joint resource allocation and task offloading with user association for URLLC and eMBB 5G multi-RAT systems 认领 引用
10
作者 Dongjun Jung Jong-Moon Chung Hea-Sook Park 《Digital Communications and Networks》 SCIE EI CSCD 2026年第4期662-676,共15页
In this paper,a multi-agent deep reinforcement learning-based joint resource allocation and task offloading control technique is proposed to minimize the energy consumption of user equipment(UE)supporting 5G ultra rel... In this paper,a multi-agent deep reinforcement learning-based joint resource allocation and task offloading control technique is proposed to minimize the energy consumption of user equipment(UE)supporting 5G ultra reliability low latency communications(URLLC)and enhanced mobile broadband(eMBB)services while meeting strict quality of service(QoS)requirements in 5G multi-radio access technology(RAT)networks.An optimization problem involving transmission power,channel resource,user association,offloading rate,and central processing unit(CPU)frequency is formulated using a queueing system-based mathematical design to support services with different characteristics while minimizing the energy consumption.It is proven in this paper that this problem is nondeterministic polynomial(NP)hard,in which multi-agent deep reinforcement learning(DRL)is used to solve the problem.To increase the learning efficiency and stability of deep reinforcement learning,prioritized experience replay(PER)and delayed target network and policy updates are applied.Simulation results show that the proposed scheme provides an improved energy consumption performance compared to the benchmarked schemes. 展开更多
关键词 Enhanced mobile broadband Multi-agent deep reinforcement learning Multi-access edge computing Resource allocation Ultra reliability low-latency communication
暂未订购 下载PDF
Dynamic Resource Allocation for Multi-Priority Requests Based on Deep Reinforcement Learning in Elastic Optical Network 认领 引用
11
作者 Zhou Yang Yang Xin +1 位作者 Sun Qiang Yang Zhuojia 《China Communications》 SCIE EI CSCD 2026年第2期312-327,共16页
As the types of traffic requests increase,the elastic optical network(EON)is considered as a promising architecture to carry multiple types of traffic requests simultaneously,including immediate reservation(IR)and adv... As the types of traffic requests increase,the elastic optical network(EON)is considered as a promising architecture to carry multiple types of traffic requests simultaneously,including immediate reservation(IR)and advance reservation(AR).Various resource allocation schemes for IR/AR requests have been designed in EON to reduce bandwidth blocking probability(BBP).However,these schemes do not consider different transmission requirements of IR requests and cannot maintain a low BBP for high-priority requests.In this paper,multi-priority is considered in the hybrid IR/AR request scenario.We modify the asynchronous advantage actor critic(A3C)model and propose an A3C-assisted priority resource allocation(APRA)algorithm.The APRA integrates priority and transmission quality of IR requests to design the A3C reward function,then dynamically allocates dedicated resources for different IR requests according to the time-varying requirements.By maximizing the reward,the transmission quality of IR requests can be matched with the priority,and lower BBP for high-priority IR requests can be ensured.Simulation results show that the APRA reduces the BBP of high-priority IR requests from 0.0341 to0.0138,and the overall network operation gain is improved by 883 compared to the scheme without considering the priority. 展开更多
关键词 deep reinforcement learning dynamic resource allocation elastic optical network multipriority requests
暂未订购 下载PDF
Optimal Resource Allocation in a Bacterial Growth Model Under Cold Stress and Temperature 认领 引用
12
作者 Saira Batool Muhammad Imran Brett McKinney 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第3期844-869,共26页
Bacterial growth requires strategic allocation of limited intracellular resources,especially under cold stress,where stabilized messenger ribonucleic acid(mRNA)secondary structures slow translation by impairing riboso... Bacterial growth requires strategic allocation of limited intracellular resources,especially under cold stress,where stabilized messenger ribonucleic acid(mRNA)secondary structures slow translation by impairing ribosome binding.Escherichia coli(E.coli)counters this bottleneck by inducing the cold-shock protein A(CspA),an RNA chaperone that remodels inhibitory structures.However,synthesizing CspA diverts biosynthetic capacity from ribosome production and metabolism,creating a fundamental resource-allocation trade-off.In this work,we develop a dynamical model capturing the interplay between metabolic precursors,ribosomes,and CspA,and use it to examine how growth and allocation patterns shift with temperature.Steady-state analysis shows that each temperature produces a distinct,locally stable equilibrium,illustrating how cold environments reshape cellular priorities.We then formulate growth maximization as an optimal control problem,solved using Pontryagin’s Maximum Principle,to identify allocation strategies that balance translation maintenance and biomass production.The resulting optimal strategies exhibit bang-bang and singular structures,highlighting periods of extreme and intermediate allocation that reflect how bacteria might dynamically prioritize competing cellular functions.These control patterns converge to their corresponding steady state allocations and provide quantitative insight into optimal resource management under cold stress.These results provide a quantitative optimal-control framework linking RNA-level cold-shock adaptation to proteome allocation and growth,yielding testable predictions for how bacteria balance translational maintenance and biomass production at suboptimal temperatures. 展开更多
关键词 Bacterial growth resource allocation nonlinear dynamical systems singular regime optimal control analysis
暂未订购 下载PDF
A3TD:A Deep Reinforcement Learning Algorithm for Joint Resource Allocation in RIS-Aided CNOMA-D2D Networks 认领 引用
13
作者 Zongchuan Li Chen Sun Jian Shu 《Computers, Materials & Continua》 SCIE EI 2026年第7期1900-1920,共21页
This paper investigates the joint resource allocation problem in Reconfigurable Intelligent Surface(RIS)-assisted cooperative non-orthogonal multiple access device-to-device(CNOMA-D2D)cellular networks.To tackle the h... This paper investigates the joint resource allocation problem in Reconfigurable Intelligent Surface(RIS)-assisted cooperative non-orthogonal multiple access device-to-device(CNOMA-D2D)cellular networks.To tackle the high-dimensional non-convex joint optimization of power control,RIS phase configuration and channel assignment,we propose an integrated user pairing strategy,PIP-UP,quantifying utility through factors,phase alignment,interference suppression and power difference,neglected in existing methods.Furthermore,we develop a hybrid deep reinforcement learning algorithm,A3TD,combining the parallel exploration capability of Asynchronous Advantage Actor-Critic(A3C)with the stable continuous optimization of Twin Delayed Deep Deterministic Policy Gradient(TD3).This integration enables efficient and robust joint optimization of D2D channel allocation,transmit power,and RIS phase shifts.Simulation results demonstrate that the proposed A3TD algorithm significantly outperforms baseline algorithms,Actor-Critic(AC),Deep Deterministic Policy Gradient(DDPG)and TD3,in terms of sum rate and convergence speed,validating its effectiveness for resource management in complex RIS-assisted CNOMA-D2D networks. 展开更多
关键词 Reconfigurable intelligent surface cooperative non-orthogonal multiple access device-to-device communication resource allocation deep reinforcement learning user pairing
暂未订购 下载PDF
Admissible Heuristic Design for Optimally Scheduling Resource Allocation Systems With Generalized Timed Petri Nets 认领 引用
14
作者 Yuanzheng Xiao Yangqing Gao +1 位作者 Haoran Wu Bo Huang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第7期1572-1583,共12页
The heuristic function in Petri-net-based A*search directly influences both the search efficiency and solution quality for scheduling resource allocation systems(RASs).In the literature,some heuristic functions have b... The heuristic function in Petri-net-based A*search directly influences both the search efficiency and solution quality for scheduling resource allocation systems(RASs).In the literature,some heuristic functions have been proposed,but most of them fail to consider key aspects such as token remaining time,alternative routes,weighted arcs,multiple resource copies,and batch processing ability,which are common in the place-timed Petri nets(PNs)for RASs.This paper proposes two novel heuristic functions.Both are admissible,guaranteeing the optimality of the obtained schedules.In addition,they are designed not only for ordinary PNs but also for generalized ones,which may have arc weights greater than one.They can effectively handle RAS PNs with alternative routes,weighted arcs,multiple resource copies,and batch processing capability.Most importantly,the new heuristics,especially the second one,are highly informed,leading to faster searches for optimal schedules compared to existing heuristics for generalized PNs.Experiments on several benchmark PNs of RASs have been conducted to demonstrate the effectiveness and efficiency of our methods. 展开更多
关键词 A*search heuristic function resource allocation system scheduling timed Petri net
暂未订购 下载PDF
Joint Optimization of Routing and Resource Allocation in Decentralized UAV Networks Based on DDQN and GNN 认领 引用
15
作者 Nawaf Q.H.Othman YANG Qinghai JIANG Xinpei 《电讯技术》 北大核心 2026年第1期1-10,共10页
Optimizing routing and resource allocation in decentralized unmanned aerial vehicle(UAV)networks remains challenging due to interference and rapidly changing topologies.The authors introduce a novel framework combinin... Optimizing routing and resource allocation in decentralized unmanned aerial vehicle(UAV)networks remains challenging due to interference and rapidly changing topologies.The authors introduce a novel framework combining double deep Q-networks(DDQNs)and graph neural networks(GNNs)for joint routing and resource allocation.The framework uses GNNs to model the network topology and DDQNs to adaptively control routing and resource allocation,addressing interference and improving network performance.Simulation results show that the proposed approach outperforms traditional methods such as Closest-to-Destination(c2Dst),Max-SINR(mSINR),and Multi-Layer Perceptron(MLP)-based models,achieving approximately 23.5% improvement in throughput,50% increase in connection probability,and 17.6% reduction in number of hops,demonstrating its effectiveness in dynamic UAV networks. 展开更多
关键词 decentralized UAV network resource allocation routing algorithm GNN DDQN DRL
暂未订购 下载PDF
Research on the Financial Path of Optimizing Resource Allocation at the Prefectural Level in Jiangsu from the Perspective of Urban-Rural Integration Development:A Case Study of Xuzhou City 认领 引用
16
作者 Yanglong Feng 《Proceedings of Business and Economic Studies》 2026年第3期129-136,共8页
Taking Xuzhou in Jiangsu as the research object,this paper deeply explores the optimization path of prefectural-level financial resource allocation under the background of urban-rural integration development.Through t... Taking Xuzhou in Jiangsu as the research object,this paper deeply explores the optimization path of prefectural-level financial resource allocation under the background of urban-rural integration development.Through the research on the current situation and practical strategies of financial construction in Xuzhou,this paper finds that financial resources can support urban-rural integration development by optimizing factor allocation,upgrading industrial synergy,and improving services and ecology.Innovative financial products proposed by Xuzhou,such as“Cold Storage Loan”and“Equipment Guarantee”,have shown effects in promoting the flow of urban-rural factors and industrial integration.However,the Xuzhou region is still facing problems such as weak rural financial infrastructure,insufficient coverage of inclusive finance,and obstacles to property right mortgage and circulation.In this regard,this paper puts forward multiple strategies to enhance financial support for urban-rural integration development,so as to provide reference for financial support for urban-rural integration development in similar regions in Jiangsu and even the whole country. 展开更多
关键词 Urban-Rural Integration Jiangsu Xuzhou Prefectural Level Resource Allocation Finance
暂未订购 下载PDF
On Optimizing Resource Allocation:A Comparative Review of Resource Allocation Strategies in HetNets 认领 引用 被引量：1
17
作者 Jeta Dobruna Zana Limani Fazliu +1 位作者 Iztok Humar Mojca Volk 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第3期2211-2245,共35页
Resource allocation remains a challenging issue in communication networks,and its complexity is continuously increasing with the densification of the networks.With the evolution of new wireless technologies such as Fi... Resource allocation remains a challenging issue in communication networks,and its complexity is continuously increasing with the densification of the networks.With the evolution of new wireless technologies such as Fifth Generation(5G)and Sixth Generation(6G)mobile networks,the service level requirements have become stricter and more heterogeneous depending on the use case.In this paper,we review a large body of literature on various resource allocation schemes that are used in particular in mobile wireless communication networks and compare the proposed schemes in terms of performance indicators as well as techniques used.Our review shows that among the strategies proposed in the literature,there is a wide variety of optimization targets and combinations thereof,focusing mainly on performance indicators such as energy efficiency,spectral efficiency,and network capacity.In addition,in this paper,selected algorithms for resource allocation are numerically analyzed through simulations to compare and highlight the importance of how the resource algorithms are implemented to achieve efficient usage of the available spectrum.The performance of selected algorithms is evaluated in a multi-cell heterogeneous network and compared to proportional fair and eICIC,a widely-used combination of resource allocation and interferencemitigation techniques used by communication networks.The results show that one approach may performbetter when looking at the individual average user data rate but worse when looking at the overall spectral or energy efficiency,depending on the category of traffic.The results,therefore,confirm that theremay not be a single algorithmthat visibly outperforms other candidates in terms of all performance criteria.Instead,their efficiency is always a consequence of a strategic choice of goals,and the targeted parameters are optimized at a price.Thus,the development and implementation of resource allocation algorithms must follow concrete usage scenarios and network needs and be highly dependent on the requirements and criteria of network performance. 展开更多
关键词 Resource allocation wireless communication heterogeneous networks resource allocation algorithms
暂未订购 下载PDF
DDPG-Based Intelligent Computation Offloading and Resource Allocation for LEO Satellite Edge Computing Network 认领 引用 被引量：2
18
作者 Jia Min Wu Jian +2 位作者 Zhang Liang Wang Xinyu Guo Qing 《China Communications》 SCIE EI CSCD 2025年第3期1-15,共15页
Low earth orbit(LEO)satellites with wide coverage can carry the mobile edge computing(MEC)servers with powerful computing capabilities to form the LEO satellite edge computing system,providing computing services for t... Low earth orbit(LEO)satellites with wide coverage can carry the mobile edge computing(MEC)servers with powerful computing capabilities to form the LEO satellite edge computing system,providing computing services for the global ground users.In this paper,the computation offloading problem and resource allocation problem are formulated as a mixed integer nonlinear program(MINLP)problem.This paper proposes a computation offloading algorithm based on deep deterministic policy gradient(DDPG)to obtain the user offloading decisions and user uplink transmission power.This paper uses the convex optimization algorithm based on Lagrange multiplier method to obtain the optimal MEC server resource allocation scheme.In addition,the expression of suboptimal user local CPU cycles is derived by relaxation method.Simulation results show that the proposed algorithm can achieve excellent convergence effect,and the proposed algorithm significantly reduces the system utility values at considerable time cost compared with other algorithms. 展开更多
关键词 computation offloading deep deterministic policy gradient low earth orbit satellite mobile edge computing resource allocation
暂未订购 下载PDF
A joint resource allocation strategy in a radar-communication coexistence network for target tracking and user serving 认领 引用 被引量：1
19
作者 Haowei ZHANG Weijian LIU +3 位作者 Qun ZHANG Taiyong FEI Tao SONG Weike FENG 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2025年第5期350-376,共27页
With the rapid development of commercial communications,the research on Radar-Communication Coexistence(RCC)systems is becoming a hot spot.The resource allocation techniques play a crucial role in the RCC systems.A pe... With the rapid development of commercial communications,the research on Radar-Communication Coexistence(RCC)systems is becoming a hot spot.The resource allocation techniques play a crucial role in the RCC systems.A performance-driven Joint Radar-target and Communication-user Assignment,along with Power and Subchannel Allocation(JRCAPSA)strategy,is proposed for an RCC network.The optimization model aims to minimize the sum of weighted Bayesian Cramer-Rao Lower Bounds(BCRLBs)of target state estimates for radar purpose.This is subject to constraints such as the Communication Data Rate(CDR)for communication purpose,the total power budget in each RCC system,assignment relationships,and the number of available subchannels.Considering that such a problem falls into the realm of Mixed Integer Programming(MIP),a Three-stage Iteratively Augment-based Optimization Method(TIAOM)is developed.The Communication-User Assignment(CUA),Communication Subchannel Allocation(SCA),and Radar-Target Assignment(RTA)feasible solution domains are iteratively expanded based on their importance,leading to the efficient acquisition of a suboptimal solution.Simulation results show the outperformance of the proposed JRCAPSA strategy,compared to the other benchmarks and the OPTI toolbox.The results also imply that the Bayesian Cramer-Rao Lower Bound(BCRLB)is a more stringent optimization metric for the achieved Mean Square Error(MSE),compared to Mutual Information(MI)and Signal-to-Interference-Noise Ratio(SINR). 展开更多
关键词 Radar-communication coexistence Resource allocation Bayesian Cramer-Rao Lower Bound(BCRLB) Communication Data Rate(CDR) Convex optimization
暂未订购 下载PDF
Intelligent Energy-Efficient Resource Allocation for Multi-UAV-Assisted Mobile Edge Computing Networks 认领 引用 被引量：1
20
作者 Hu Han Shen Le +2 位作者 Zhou Fuhui Wang Qun Zhu Hongbo 《China Communications》 SCIE EI CSCD 2025年第4期339-355,共17页
The unmanned aerial vehicle(UAV)-assisted mobile edge computing(MEC)has been deemed a promising solution for energy-constrained devices to run smart applications with computationintensive and latency-sensitive require... The unmanned aerial vehicle(UAV)-assisted mobile edge computing(MEC)has been deemed a promising solution for energy-constrained devices to run smart applications with computationintensive and latency-sensitive requirements,especially in some infrastructure-limited areas or some emergency scenarios.However,the multi-UAVassisted MEC network remains largely unexplored.In this paper,the dynamic trajectory optimization and computation offloading are studied in a multi-UAVassisted MEC system where multiple UAVs fly over a target area with different trajectories to serve ground users.By considering the dynamic channel condition and random task arrival and jointly optimizing UAVs'trajectories,user association,and subchannel assignment,the average long-term sum of the user energy consumption minimization problem is formulated.To address the problem involving both discrete and continuous variables,a hybrid decision deep reinforcement learning(DRL)-based intelligent energyefficient resource allocation and trajectory optimization algorithm is proposed,named HDRT algorithm,where deep Q network(DQN)and deep deterministic policy gradient(DDPG)are invoked to process discrete and continuous variables,respectively.Simulation results show that the proposed HDRT algorithm converges fast and outperforms other benchmarks in the aspect of user energy consumption and latency. 展开更多
关键词 dynamic trajectory optimization intelligent resource allocation unmanned aerial vehicle uav assisted uav assisted mec energy efficiency smart applications mobile edge computing mec deep reinforcement learning
暂未订购 下载PDF
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
上一页 1 2 … 34 下一页 到第 页
在线咨询 使用帮助 返回顶部 意见反馈