Deep Architectures for Modulation Recognition

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来源：
2017 IEEE INTERNATIONAL SYMPOSIUM ON DYNAMIC SPECTRUM ACCESS NETWORKS (IEEE DYSPAN) | 2017年
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中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
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 radio 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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页数：6
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