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Automatic Modulation Classification Based on Decentralized Learning and Ensemble Learning
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DOI：10.1109/TVT.2022.3164935.png)
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Abstract
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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.
Keywords:
Training
Modulation
Testing
Servers
Load modeling
Broadcasting
Hybrid learning
Automatic modulation classification (AMC)
decentralized learning
ensemble learning
Journal
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7.1
Papers:
1.8W
Citations:
6.6W
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Cited Papers
Deep Neural Network Compression Technique Towards Efficient Digital Signal Modulation Recognition in Edge Device.png)
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