Journal
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
Volume 69, Issue 5, Pages 5703-5706Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TVT.2020.2983143
Keywords
Automatic modulation classification; deep learning; edge device; network pruning
Categories
Funding
- Fundamental Research Funds for the Central Universities [61771154]
- [HEUCFG201830]
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Automatic modulation classification (AMC) plays an important role in both civilian and military applications. Today, increasingly more researchers apply a deep learning framework in AMC. However, few papers take into account that a typical deep model is difficult to deploy on resource constrained devices. In this paper, we propose a new filter-level pruning technique based on activation maximization (AM) that omits the less important convolutional filter. Compared to other network pruning techniques, the convolutional neural network pruned via the AM method achieves equal or higher classification accuracy in the RadioML2016.10a dataset.
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