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Evidence record 103 · automatically gathered

DKDNet: Dual Knowledge and Data-Driven Network for Cross-Domain Automatic Modulation Classification

The dynamics of communication environments induce significant distribution shifts across domains, challenging the generalization of deep learning-based automatic modulation classification (AMC) models. While existing UDA methods alleviate this problem by aligning source and target features, they give limited consideration to modulation-specific structures that remain informative across domain conditions. In this paper, we consider signal prior knowledge, grounded in communication protocols and p

Record details

Published: 9 July 2026
Source: arXiv
Category: Research
Topics: Environment
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (9 July 2026), “DKDNet: Dual Knowledge and Data-Driven Network for Cross-Domain Automatic Modulation Classification,” evidence record 103, https://ethics.ai/record/103 (originally published by arXiv).

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