Joint Multi-objective Optimization for Radio Access Network Slicing Using Multi-agent Deep Reinforcement Learning

Radio access network (RAN) slices can provide various customized services for next-generation wireless networks. Thus, multiple performance metrics of different types of RAN slices need to be jointly optimized. However, existing efforts in multi-objective optimization problem (MOOP) for RAN slicing...

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Veröffentlicht in:	IEEE transactions on vehicular technology Jg. 72; H. 9; S. 1 - 16
Hauptverfasser:	Zhou, Guorong, Zhao, Liqiang, Zheng, Gan, Xie, Zhijie, Song, Shenghui, Chen, Kwang-Cheng
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.09.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Delays Heuristic algorithms Hierarchies multi-agent deep reinforcement learning multi-objective optimization Multiagent systems Multiple objective analysis Network slicing non-scalarization Optimization Pareto optimization Pareto optimum Performance measurement Polynomials Radio access network slicing rank voting method Resource management Throughput Wireless networks
ISSN:	0018-9545, 1939-9359
Online-Zugang:	Volltext
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