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arXiv 1810.02027eess.SPcs.ITcs.LGmath.IT
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Polar Feature Based Deep Architectures for Automatic Modulation Classification Considering Channel Fading

Graduate Institute of Electrical Engineering, National Taiwan University(台湾大学电机工程学研究所)

Chieh-Fang Teng, Ching-Chun Liao, Chun-Hsiang Chen, An-Yeu Wu

英文摘要:

To develop intelligent receivers, automatic modulation classification (AMC) plays an important role for better spectrum utilization. The emerging deep learning (DL) technique has received much attention in AMC due to its superior performance in classifying data with deep structure. In this work, a novel polar-based deep learning architecture with channel compensation network (CCN) is proposed. Our test results show that learning features from polar domain (r-theta) can improve recognition accuracy by 5% and reduce training overhead by 48%. Besides, the proposed CCN is also robust to channel fading, such as amplitude and phase offsets, and can improve the recognition accuracy by 14% under practical channel environments.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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