A DRL-Based Automated Algorithm Selection Framework for Cross-Layer QoS-Aware Scheduling and Antenna Allocation in Massive MIMO Systems

Massive multiple-input-multiple-output (MIMO) systems support advanced applications with high data rates, low latency, and high reliability in next-generation mobile networks. However, using machine learning in massive MIMO resource allocation is challenging due to quality of service (QoS) and netwo...

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Vydáno v:IEEE access Ročník 11; s. 1
Hlavní autoři: Huang, Chih-Wei, Althamary, Ibrahim, Chou, Yen-Cheng, Chen, Hong-Yunn, Chou, Cheng-Fu
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.01.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Antennas
automated algorithm selection
Cross layer design
Deep learning
deep reinforcement learning
Heuristic algorithms
Machine learning
Markov processes
Massive MIMO
MIMO communication
Mobile network
Network latency
Network reliability
Precoding
QoS
Quality of service
Quality of service architectures
Resource allocation
Resource management
Scheduling
Traffic models
User satisfaction
ISSN:2169-3536, 2169-3536
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Shrnutí:Massive multiple-input-multiple-output (MIMO) systems support advanced applications with high data rates, low latency, and high reliability in next-generation mobile networks. However, using machine learning in massive MIMO resource allocation is challenging due to quality of service (QoS) and network complexity across layers. This work presents a novel framework for adapting the scheduling and antenna allocation in massive MIMO systems using deep reinforcement learning (DRL). Rather than directly assigning execution parameters, the proposed framework utilizes DRL to select combinations of algorithms based on the current traffic conditions. The DRL model is trained using a specialized Markov decision process (MDP) model with a componentized action structure and is evaluated in realistic traffic scenarios. The results show that the proposed framework increases satisfied users by 7.2% and 12.5% compared to static algorithm combinations and other cross-layer adaptation methods. This demonstrates the effectiveness of the framework in managing and optimizing resource allocation in a flexible and adaptable manner.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2023.3243068