Deep Reinforcement Learning Based Massive Access Management for Ultra-Reliable Low-Latency Communications

With the rapid deployment of the Internet of Things (IoT), fifth-generation (5G) and beyond 5G networks are required to support massive access of a huge number of devices over limited radio spectrum radio. In wireless networks, different devices have various quality-of-service (QoS) requirements, ra...

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Veröffentlicht in:	IEEE transactions on wireless communications 2021-05, Vol.20 (5), p.2977-2990
Hauptverfasser:	Yang, Helin, Xiong, Zehui, Zhao, Jun, Niyato, Dusit, Yuen, Chau, Deng, Ruilong
Format:	Artikel
Sprache:	eng
Schlagworte:	Access control Cooperative learning deep distributed cooperative learning Deep learning Electronic devices Energy efficiency Internet of Things Learning systems massive access multi-agent reinforcement learning Multiagent systems Network latency Optimization Power control Quality of service Quality of service architectures Radio spectra Reinforcement learning Simulation spectrum and power management Transfer learning URLLC Wireless networks
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