MoEformer uses temporal resampling, input-dependent gating, and RoPE in a Transformer to achieve 63.74%, 66.24%, and 64.22% average accuracy on RadioML2016.10a, 2016.10b, and 2018.01A benchmarks.
Enhancing automatic modulation recognition through robust global feature extraction,
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Mixture-of-Experts Transformer for Automatic Modulation Recognition
MoEformer uses temporal resampling, input-dependent gating, and RoPE in a Transformer to achieve 63.74%, 66.24%, and 64.22% average accuracy on RadioML2016.10a, 2016.10b, and 2018.01A benchmarks.