Dualformer: Efficient Feature Extractor for Complex-valued Blind Communication Signal Analysis

arXiv cs.LG1mo4 min read

arXiv:2606.31352v1 Announce Type: new Abstract: Designing effective feature extractors is critical for blind signal analysis tasks such as automatic modulation recognition (AMR), signal scheme recognition (SSR), and \color{black} signal structure parsing (SSP). In this work, we propose dual-channel neural network (DualNN) that efficiently exploits complex-valued signals through parameter sharing across IQ channels. Unlike traditional real-valued or complex-valued models, DualNN is a groundbreaking framework which shares the network parameters for processing the real and imaginary parts of the

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Dualformer: Efficient Feature Extractor for Complex-valued Blind Communication Signal Analysis

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