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
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Zusammenfassung: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 the flexibility to support emerging applications, such as Extended Reality (XR) and Virtual Reality (VR), which demand simultaneous guarantees for high data rates, ultra low latency, and high reliability. These stringent and multidimensional QoS needs call for more intelligent and adaptive solutions. In this paper, we propose QDRLLA (QoS-aware Deep Reinforcement Learning-based Link Adaptation), a novel framework that employs deep reinforcement learning to dynamically adjust key link parameters, including modulation and coding schemes, transmission power, and subcarrier spacing, based on the QoS requirements of each application. QDRLLA learns from the environment and past observations to make informed decisions that go beyond conventional heuristic-based methods. Through extensive simulations, we demonstrate that QDRLLA significantly improves compliance with QoS targets across a range of network conditions and application types. It also improves energy efficiency by avoiding unnecessary retransmissions and optimizing resource usage. These results underscore the effectiveness of QDRLLA in supporting the complex service requirements of next-generation wireless networks.
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ISSN:2644-125X
2644-125X
DOI:10.1109/OJCOMS.2025.3593836