QoS-Aware Link Adaptation for Beyond 5G Networks: A Deep Reinforcement Learning Approach

Modern wireless communication systems face increasingly complex challenges due to rapidly changing channel conditions and the growing diversity of application-specific Quality of Service (QoS) requirements. Traditional link adaptation mechanisms primarily aim to maximize throughput and often lack th...

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Veröffentlicht in:	IEEE open journal of the Communications Society Jg. 6; S. 6368 - 6382
Hauptverfasser:	Parsa, Ali, Moghim, Neda, Shetty, Sachin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	5G mobile communication Adaptation Adaptation models B5G Deep learning deep Q-network Deep reinforcement learning Downlink Interference link adaptation machine learning modulation and coding schemes Network latency Quality of service Quality of service architectures Reliability Signal to noise ratio Throughput Ultra reliable low latency communication Virtual reality Wireless communication systems Wireless networks
ISSN:	2644-125X, 2644-125X
Online-Zugang:	Volltext
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