Showing 261 - 280 results of 1,377 for search '(( ((resource OR source) OR resources) allocation algorithm ) OR ( sources allocation algorithm ))', query time: 0.19s Refine Results
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    A Task Offloading and Resource Allocation Strategy Based on Multi-Agent Reinforcement Learning in Mobile Edge Computing by Guiwen Jiang, Rongxi Huang, Zhiming Bao, Gaocai Wang

    Published 2024-09-01
    “…Task offloading and resource allocation is a research hotspot in cloud-edge collaborative computing. …”
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    Article
  3. 263

    Energy Aware Optimal Resource Allocation in Backhaul Constraint Wireless Networks: A Two Base Stations Scenario by Yuan Gao, Peng Xue, Yi Li, Hongyi Yu, Xianfeng Wang, Shihai Gao

    Published 2015-08-01
    “…Simulations show that this allocation algorithm can improve the system capacity and energy efficiency significantly compared with the blind alternatives.…”
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    Article
  4. 264

    Energy-Efficient Multi-Agent Deep Reinforcement Learning Task Offloading and Resource Allocation for UAV Edge Computing by Shu Xu, Qingjie Liu, Chengye Gong, Xupeng Wen

    Published 2025-05-01
    “…This paper proposes a novel multi-agent reinforcement learning framework, termed Multi-Agent Twin Delayed Deep Deterministic Policy Gradient for Task Offloading and Resource Allocation (MATD3-TORA), to optimize task offloading and resource allocation in UAV-assisted MEC networks. …”
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    Article
  5. 265

    Power-Efficient UAV Positioning and Resource Allocation in UAV-Assisted Wireless Networks for Video Streaming with Fairness Consideration by Zaheer Ahmed, Ayaz Ahmad, Muhammad Altaf, Mohammed Ahmed Hassan

    Published 2025-05-01
    “…The joint optimization includes power minimization, efficient resource allocation, i.e., transmit power and bandwidth, and efficient two-dimensional positioning of the UAV while meeting system constraints. …”
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    Article
  6. 266

    Network Resource Allocation Method Based on Awareness–Prediction Joint Compensation for Low-Earth-Orbit Satellite Networks by Hang Di, Tao Dong, Zhihui Liu, Shuotong Wei, Qiwei Zhang, Dingyun Zhang

    Published 2025-05-01
    “…Furthermore, an efficient, accelerated alternating-direction method of multipliers (ADMM) resource allocation algorithm is proposed with the aim of maximizing the satisfaction of service resources requirements. …”
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    Article
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    SPYDER: QoS-Aware Radio Resource Allocation in Multiuser ISAC-Capable C-V2X Networks by Syed Najaf Haider Shah, Aamir Ullah Khan, Christian Schneider, Joerg Robert

    Published 2025-01-01
    “…To counteract this, we employ sparse reconstruction algorithms within the compressed sensing framework, enhancing flexibility in TF resource allocation and providing high-resolution radar sensing despite uncoordinated resource selection. …”
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    Article
  9. 269

    Mode selection and resource optimization for UAV-assisted cellular networks by Daquan FENG, Canjian ZHENG, Xiangqi KONG

    Published 2024-03-01
    “…The resource allocation and optimization scheme was studied in a coexistence scenario of unmanned aerial vehicle (UAV) and cellular communication network.To improve spectrum efficiency of the system, UAV users could reuse the cellular spectrum resources to access the network through full duplex or half duplex device-to-device technique.Additionally, a joint access control, mode selection, power control and resource allocation optimization problem was formulated to maximize the overall throughput of the network while ensuring quality of service requirements for both UAV users and ground cellular users.Specifically, the phase 1 method in the convex optimization was adopted for access control and feasibility check, and then the convex and concave procedure (CCCP) iterative algorithm was used to solve the power control problem for feasible UAV user pairs.By using this local optimum value, the original optimization problem can be simplified into a weighted maximization problem.Finally, the Kuhn-Munkres (KM) algorithm was used to match the optimal channel resources and obtain the global optimal throughput value of the system.Numerical results show that the proposed scheme can significantly improve the performance of system.…”
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    Article
  10. 270
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    Energy-saving computation offloading scheme based on Sarsa algorithm in industrial internet of things by Jun SUN, Shangweikang ZHAO

    Published 2022-09-01
    “…In order to reduce the total energy consumption of industrial internet of things systems with deadline requirements, a computing offloading scheme based on Sarsa algorithm was proposed.Based on the characteristics of Sarsa algorithm, the energy consumption optimization problem was coupled.The Sarsa algorithm iterated the external state value function, selected the appropriate edge computing server to offload the computing task, and then the particle swarm optimization algorithm solved the resource allocation problem.Finally, the environmental information was updated for the next round of algorithm iteration until the algorithm met the end conditions.The simulation results show that this scheme has faster convergence speed and effectively reduces the system energy consumption.…”
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    Article
  12. 272

    Deep Reinforcement Learning for Resource Constrained Multiclass Scheduling in Wireless Networks by Apostolos Avranas, Philippe Ciblat, Marios Kountouris

    Published 2023-01-01
    “…Our method can, for instance, achieve with 13% less power and bandwidth resources the same user satisfaction rate as a myopic algorithm using knapsack optimization.…”
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    Article
  13. 273
  14. 274

    Optimal allocation of urban land space based on NSGA2 by Yi Guo, Chaoqin Bai, Peiwen Zhao

    Published 2025-03-01
    “…Urban land spatial optimization is one of the important issues in urban planning and land resource management. As the speed advancement of urbanization and the continuous increase of population, the rational use of land resources has become the key to sustainable urban development. …”
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    Deep reinforcement learning-based resource reservation algorithm for emergency Internet-of-things slice by Guolin SUN, Ruijie OU, Guisong LIU

    Published 2020-09-01
    “…Based on the requirements of ultra-low latency services for emergency Internet-of-things (EIoT) applications,a multi-slice network architecture for ultra-low latency emergency IoT was designed,and a general methodology framework based on resource reservation,sharing and isolation for multiple slices was proposed.In the proposed framework,real-time and automatic inter-slice resource demand prediction and allocation were realized based on deep reinforcement learning (DRL),while intra-slice user resource allocation was modeled as a shape-based 2-dimension packing problem and solved with a heuristic numerical algorithm,so that intra-slice resource customization was achieved.Simulation results show that the resource reservation-based method enable EIoT slices to explicitly reserve resources,provide a better security isolation level,and DRL could guarantee accuracy and real-time updates of resource reservations.Compared with four existing algorithms,dueling deep Q-network (DQN) performes better than the benchmarks.…”
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  17. 277

    Designing a Multiobjective Human Resource Scheduling Model Using the Tabu Search Algorithm by Roghayeh Mirataollahi olya, Seyed Ahmad Shayannia, Mohammad Mehdi movahedi

    Published 2022-01-01
    “…In this research, considering the high importance of these issues, the problems of scheduling and allocation of manpower in a real place are solved. To this end, the metaheuristic Tabu search algorithm is used with the aim of minimizing the duration of activity and the presence of all manpower. …”
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    Article
  18. 278

    A FPGA Accelerator of Distributed A3C Algorithm with Optimal Resource Deployment by Fen Ge, Guohui Zhang, Ziyu Li, Fang Zhou

    Published 2024-01-01
    “…In addition, the resource wastage problem caused by the distributed training characteristics of A3C algorithms and the resource allocation problem affected by the imbalance between the computational amount of inference and training need to be carefully considered when designing accelerators. …”
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