调制识别的深度体系结构

2017 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN) Pub Date : 2017-03-06 DOI:10.1109/DySPAN.2017.7920754

Nathan E. West, Tim O'Shea

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引用次数: 303

摘要

我们通过将深度神经网络应用于无线电调制识别任务来研究机器学习的最新进展。结果表明，比例调制识别不受网络深度的限制，进一步的工作应集中在改进学习同步和均衡。这些领域的进步可能来自为这些任务设计的新架构或通过新的训练方法。

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Deep architectures for modulation recognition

We survey the latest advances in machine learning with deep neural networks by applying them to the task of radio modulation recognition. Results show that ratio modulation recognition is not limited by network depth and further work should focus on improving learned synchronization and equalization. Advances in these areas will likely come from novel architectures designed for these tasks or through novel training methods.

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来源期刊

2017 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN)

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