Researchers have developed a novel unified framework for automatic modulation recognition (AMR) that addresses the challenges posed by varying sequence lengths. This framework utilizes a common convolutional state-space backbone paired with specialized interfaces for short and long sequences. For shorter sequences, relation tokens are introduced to mitigate information loss, while for longer sequences, a gated multi-scale residual refinement module and a fixed-averaging classifier collaboration are employed. The proposed method achieved high average accuracies of 67.28% on RML2016.10b and 87.19% on HisarMod2019, demonstrating the effectiveness of expert-interface decoupling over traditional one-size-fits-all architectures. AI
IMPACT This research could lead to more robust and adaptable signal processing systems in communication technologies.
RANK_REASON The cluster contains an academic paper detailing a new technical approach. [lever_c_demoted from research: ic=1 ai=0.7]
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