Multiagent Deep-Reinforcement-Learning-Based Resource Allocation for Heterogeneous QoS Guarantees for Vehicular Networks

Vehicle-to-vehicle communications can offer direct information interaction, including security-centered information and entertainment information. However, the rapid proliferation of vehicles and the diversity of communications services demand for a more intelligent and efficient resource allocation...

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Veröffentlicht in:	IEEE internet of things journal Jg. 9; H. 3; S. 1683 - 1695
Hauptverfasser:	Tian, Jie, Liu, Qianqian, Zhang, Haixia, Wu, Dalei
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.02.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Copper Deep learning Deep reinforcement learning (DRL) Entertainment Entertainment industry heterogeneous applications Machine learning multi-agent deep deterministic policy gradient (MADDPG) Multiagent systems Network latency Optimization Power control Quality of service Quality of service architectures Reinforcement learning Resource allocation Resource management Training Vehicles
ISSN:	2327-4662, 2327-4662
Online-Zugang:	Volltext
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