FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3010301175> ?p ?o ?g. }

• W3010301175 endingPage "2428" @default.
• W3010301175 startingPage "2416" @default.
• W3010301175 abstract "In this paper, we study joint allocation of the spectrum, computing, and storing resources in a multi-access edge computing (MEC)-based vehicular network. To support different vehicular applications, we consider two typical MEC architectures and formulate multi-dimensional resource optimization problems accordingly, which are usually with high computation complexity and overlong problem-solving time. Thus, we exploit reinforcement learning (RL) to transform the two formulated problems and solve them by leveraging the deep deterministic policy gradient (DDPG) and hierarchical learning architectures. Via off-line training, the network dynamics can be automatically learned and appropriate resource allocation decisions can be rapidly obtained to satisfy the quality-of-service (QoS) requirements of vehicular applications. From simulation results, the proposed resource management schemes can achieve high delay/QoS satisfaction ratios." @default.
• W3010301175 created "2020-03-13" @default.
• W3010301175 creator A5022812128 @default.
• W3010301175 creator A5025221904 @default.
• W3010301175 date "2020-10-01" @default.
• W3010301175 modified "2023-10-01" @default.
• W3010301175 title "Deep Reinforcement Learning Based Resource Management for Multi-Access Edge Computing in Vehicular Networks" @default.
• W3010301175 cites W2046376809 @default.
• W3010301175 cites W2094387729 @default.
• W3010301175 cites W2562947506 @default.
• W3010301175 cites W2615926310 @default.
• W3010301175 cites W2735764544 @default.
• W3010301175 cites W2756144809 @default.
• W3010301175 cites W2761862361 @default.
• W3010301175 cites W2766606340 @default.
• W3010301175 cites W2772526503 @default.
• W3010301175 cites W2786070938 @default.
• W3010301175 cites W2795543364 @default.
• W3010301175 cites W2808260862 @default.
• W3010301175 cites W2856725472 @default.
• W3010301175 cites W2883265677 @default.
• W3010301175 cites W2885703553 @default.
• W3010301175 cites W2889540440 @default.
• W3010301175 cites W2891353362 @default.
• W3010301175 cites W2891819550 @default.
• W3010301175 cites W2895939936 @default.
• W3010301175 cites W2898035736 @default.
• W3010301175 cites W2898652425 @default.
• W3010301175 cites W2903874029 @default.
• W3010301175 cites W2905685817 @default.
• W3010301175 cites W2906647810 @default.
• W3010301175 cites W2924948991 @default.
• W3010301175 cites W2938135176 @default.
• W3010301175 cites W2944050366 @default.
• W3010301175 cites W2947829929 @default.
• W3010301175 cites W2952896628 @default.
• W3010301175 cites W2954039338 @default.
• W3010301175 cites W2963000651 @default.
• W3010301175 cites W2963536698 @default.
• W3010301175 cites W2963620705 @default.
• W3010301175 cites W2963676785 @default.
• W3010301175 cites W2964048168 @default.
• W3010301175 cites W2964298328 @default.
• W3010301175 cites W2968424451 @default.
• W3010301175 cites W2982650630 @default.
• W3010301175 cites W2998578058 @default.
• W3010301175 cites W3099769178 @default.
• W3010301175 doi "https://doi.org/10.1109/tnse.2020.2978856" @default.
• W3010301175 hasPublicationYear "2020" @default.
• W3010301175 type Work @default.
• W3010301175 sameAs 3010301175 @default.
• W3010301175 citedByCount "113" @default.
• W3010301175 countsByYear W30103011752012 @default.
• W3010301175 countsByYear W30103011752020 @default.
• W3010301175 countsByYear W30103011752021 @default.
• W3010301175 countsByYear W30103011752022 @default.
• W3010301175 countsByYear W30103011752023 @default.
• W3010301175 crossrefType "journal-article" @default.
• W3010301175 hasAuthorship W3010301175A5022812128 @default.
• W3010301175 hasAuthorship W3010301175A5025221904 @default.
• W3010301175 hasConcept C111919701 @default.
• W3010301175 hasConcept C11413529 @default.
• W3010301175 hasConcept C120314980 @default.
• W3010301175 hasConcept C138236772 @default.
• W3010301175 hasConcept C154945302 @default.
• W3010301175 hasConcept C162307627 @default.
• W3010301175 hasConcept C165696696 @default.
• W3010301175 hasConcept C192448918 @default.
• W3010301175 hasConcept C2778456923 @default.
• W3010301175 hasConcept C2780609101 @default.
• W3010301175 hasConcept C29202148 @default.
• W3010301175 hasConcept C31258907 @default.
• W3010301175 hasConcept C38652104 @default.
• W3010301175 hasConcept C41008148 @default.
• W3010301175 hasConcept C45374587 @default.
• W3010301175 hasConcept C5119721 @default.
• W3010301175 hasConcept C555944384 @default.
• W3010301175 hasConcept C76155785 @default.
• W3010301175 hasConcept C79974875 @default.
• W3010301175 hasConcept C94523657 @default.
• W3010301175 hasConcept C97541855 @default.
• W3010301175 hasConceptScore W3010301175C111919701 @default.
• W3010301175 hasConceptScore W3010301175C11413529 @default.
• W3010301175 hasConceptScore W3010301175C120314980 @default.
• W3010301175 hasConceptScore W3010301175C138236772 @default.
• W3010301175 hasConceptScore W3010301175C154945302 @default.
• W3010301175 hasConceptScore W3010301175C162307627 @default.
• W3010301175 hasConceptScore W3010301175C165696696 @default.
• W3010301175 hasConceptScore W3010301175C192448918 @default.
• W3010301175 hasConceptScore W3010301175C2778456923 @default.
• W3010301175 hasConceptScore W3010301175C2780609101 @default.
• W3010301175 hasConceptScore W3010301175C29202148 @default.
• W3010301175 hasConceptScore W3010301175C31258907 @default.
• W3010301175 hasConceptScore W3010301175C38652104 @default.
• W3010301175 hasConceptScore W3010301175C41008148 @default.
• W3010301175 hasConceptScore W3010301175C45374587 @default.
• W3010301175 hasConceptScore W3010301175C5119721 @default.
• W3010301175 hasConceptScore W3010301175C555944384 @default.

• next