Automatic Modulation Classification Based on Decentralized Learning and Ensemble Learning
IEEE Transactions on Vehicular Technology, vol. 71, pp. 7942–7946
Abstract
To deal with the deep learning-based automatic modulation classification (AMC) in the scenario that the training dataset are distributed over a network without gathering the data at a centralized location, the decentralized learning-based AMC (DecentAMC) had been presented. However, there exists frequent model parameter uploading and downloading in DecentAMC method, which cause high communication overhead. In this paper, an innovative learning framework are proposed for AMC (named DeEnAMC), in which the framework is realized by utilizing the combination of decentralized learning and ensemble learning. Our results show that the proposed DeEnAMC reduces communication overhead while keeping a similar classification performance to DecentAMC.
Authors 5
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Nanjing University of Posts and Telecommunications
Affiliation as printed
College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China
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Nanjing University of Posts and Telecommunications
Affiliation as printed
College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China
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Nanjing University of Posts and Telecommunications
Affiliation as printed
College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China
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Affiliation as printed
Institute for Communication Technologies and Embedded Systems, RWTH Aachen University, Aachen, Germany
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Affiliation as printed
Resilient Wireless Communication Research Group, International Research Institute of Disaster Science (IRIDeS), Tohoku University, Sendai, Japan
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