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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Published in:	IEEE transactions on vehicular technology Vol. 72; no. 3; pp. 4114 - 4118
Main Authors:	Jiang, Jing, Wang, Jiechen, Chu, Hongyun, Gao, Qiang, Zhang, Jiayi
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.03.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	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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Abstract	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.
AbstractList	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.
Author	Jiang, Jing Zhang, Jiayi Gao, Qiang Wang, Jiechen Chu, Hongyun
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SubjectTerms	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
Title	Whale Swarm Reinforcement Learning Based Dynamic Cooperation Clustering Method for Cell-Free Massive MIMO Systems
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