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- W3190992837 abstract "Cognitive radio technology is an essential branch in wireless communication, and automatic modulation classification (AMC) is an essential component of intelligent communication systems. As the spectrum traffic becoming congested, the fast modulation classification becomes challenging. While modulation classification has been a well-studied problem, achieving high accuracy from a small number of samples is challenging. This paper builds on previous work by using an R-transformer-based network to identify a complex open-source RadioML dataset. We obtain several network structures suitable for AMC. The network consisting of CNN and attention mechanism can rely on minor parameters and minimal model size to obtain the best performance. Still, the disadvantage is the requirement of the input sequence length. In contrast, an improved-RT model can solve this problem. Compared to existing results, networks proposed in this paper can achieve better accuracy with very few required parameters." @default.
- W3190992837 created "2021-08-16" @default.
- W3190992837 creator A5085173772 @default.
- W3190992837 date "2021-06-28" @default.
- W3190992837 modified "2023-09-23" @default.
- W3190992837 title "Automatic Modulation Classification Based on Improved R-Transformer" @default.
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- W3190992837 doi "https://doi.org/10.1109/iwcmc51323.2021.9498878" @default.
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