Whale Swarm Reinforcement Learning Based Dynamic Cooperation Clustering Method for Cell-Free Massive MIMO Systems

Dynamic cooperation clustering (DCC) becomes a main enabler for cell-free massive MIMO systems since it can improve the energy efficiency and reduce the complexity of signal processing significantly. However, DCC formation for all served users simultaneously is a very complicated mixed binary nonlin...

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Veröffentlicht in:IEEE transactions on vehicular technology Jg. 72; H. 3; S. 4114 - 4118
Hauptverfasser: Jiang, Jing, Wang, Jiechen, Chu, Hongyun, Gao, Qiang, Zhang, Jiayi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.03.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Cell-free massive multiple-input multiple-output
Clustering
Clustering algorithms
Convergence
Cooperation
dynamic cooperation clustering
Massive MIMO
Nonlinear programming
Optimization
Reinforcement learning
Searching
Signal to noise ratio
spectral efficiency
Whales
ISSN:0018-9545, 1939-9359
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Zusammenfassung:Dynamic cooperation clustering (DCC) becomes a main enabler for cell-free massive MIMO systems since it can improve the energy efficiency and reduce the complexity of signal processing significantly. However, DCC formation for all served users simultaneously is a very complicated mixed binary nonlinear programming problem, a single agent has limited capability to search the optimal schemes. In this paper, we propose a whale swarm reinforcement learning (WSRL) based DCC method. Exploiting multiple searching agent imitated by a group of whales, whale swarm optimization (WOA) algorithm searches the optimal DCC scheme simultaneously and learn the searching experience from each other. Moreover, the reinforcement learning is integrated to select the most efficient hunting action for each whale, which can accelerate the convergence and avoid the local trap. Simulation results demonstrate that the proposed method has better searching ability and higher convergence speed than the existing works.
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ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2022.3222756