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Communications and Networking. 17th EAI International Conference, Chinacom 2022, Virtual Event, November 19-20, 2022, Proceedings

Research Article

MAML-Based D2D Power Control Scheme in User-Variable Scenario

Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-34790-0_28,
        author={Zhenyu Fan and Xinyu Gu},
        title={MAML-Based D2D Power Control Scheme in User-Variable Scenario},
        proceedings={Communications and Networking. 17th EAI International Conference, Chinacom 2022, Virtual Event, November 19-20, 2022, Proceedings},
        proceedings_a={CHINACOM},
        year={2023},
        month={6},
        keywords={Model-agnostic meta-learning deep neural network user-variable weight initialization optimization},
        doi={10.1007/978-3-031-34790-0_28}
    }
    
  • Zhenyu Fan
    Xinyu Gu
    Year: 2023
    MAML-Based D2D Power Control Scheme in User-Variable Scenario
    CHINACOM
    Springer
    DOI: 10.1007/978-3-031-34790-0_28
Zhenyu Fan1,*, Xinyu Gu1
  • 1: School of Artificial Intelligence, Beijing University of Posts and Telecommunications
*Contact email: salam@bupt.edu.cn

Abstract

Meta-Learning has been extensively studied since it has the ability of quickly learning new skills by leveraging prior few-shot tasks, which is capable of relieving the problem of relying on large amount of data sample existed in deep learning. In this paper, we apply an algorithm of model-agnostic meta-learning (MAML) to cope with Device-to-Device (D2D) transmit power control issue in user-variable scenario. Specifically, MAML first learns good weight initializations of D2D power control neural network in initial D2D scenario, contributing to a meta-learner. When the number of D2D user changes, the network loads the meta learner and quickly adapts to a new scenario on a few shots of samples. Simulation results demonstrate that MAML shows good performance in generalization and MAML better conducts D2D user-variety power control issues than regular deep neural network power control methods.

Keywords
Model-agnostic meta-learning deep neural network user-variable weight initialization optimization
Published
2023-06-10
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-34790-0_28
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