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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| Vydáno v: | IEEE transactions on vehicular technology Ročník 72; číslo 3; s. 4114 - 4118 |
|---|---|
| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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New York
IEEE
01.03.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| 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. |
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| 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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| Cites_doi | 10.1109/LCOMM.2020.3041640 10.1109/LWC.2017.2734893 10.1109/TCOMM.2020.3047801 10.1109/TWC.2017.2655515 10.1016/j.advengsoft.2016.01.008 10.1109/TVT.2020.2970018 10.1109/TGCN.2017.2770215 10.1109/TVT.2020.3000496 10.1109/JSYST.2019.2960088 10.1109/GLOBECOM42002.2020.9322341 10.1109/TWC.2021.3104743 10.1109/MNET.001.1900287 10.1109/JSAC.2020.3018825 10.1109/GLOBECOM46510.2021.9685221 10.1109/TCOMM.2020.2987311 10.1109/GLOCOMW.2018.8644255 |
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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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