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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Abstract	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.
AbstractList	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.
Author	Huang, Chih-Wei Chou, Yen-Cheng Althamary, Ibrahim Chou, Cheng-Fu Chen, Hong-Yunn
Author_xml	– sequence: 1 givenname: Chih-Wei orcidid: 0000-0002-0202-8977 surname: Huang fullname: Huang, Chih-Wei organization: Department of Communication Engineering, National Central University, Taoyuan, Taiwan – sequence: 2 givenname: Ibrahim surname: Althamary fullname: Althamary, Ibrahim organization: Department of Communication Engineering, National Central University, Taoyuan, Taiwan – sequence: 3 givenname: Yen-Cheng orcidid: 0000-0003-1100-8262 surname: Chou fullname: Chou, Yen-Cheng organization: Department of Communication Engineering, National Central University, Taoyuan, Taiwan – sequence: 4 givenname: Hong-Yunn surname: Chen fullname: Chen, Hong-Yunn organization: Graduate Institute of Networking and Multimedia, National Taiwan University, Taipei, Taiwan – sequence: 5 givenname: Cheng-Fu orcidid: 0000-0003-2684-5039 surname: Chou fullname: Chou, Cheng-Fu organization: Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
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SubjectTerms	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
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Title	A DRL-Based Automated Algorithm Selection Framework for Cross-Layer QoS-Aware Scheduling and Antenna Allocation in Massive MIMO Systems
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