End-to-End Radio Traffic Sequence Recognition with Recurrent Neural Networks

被引：0
|
作者：
O'Shea, Timothy J. [1 ]
Hitefield, Seth [1 ]
Corgan, Johnathan [2 ]
机构：
[1] Virginia Tech, Bradley Dept Elect & Comp Engn, Arlington, VA 22203 USA
[2] Corgan Labs, San Jose, CA USA
来源：
2016 IEEE GLOBAL CONFERENCE ON SIGNAL AND INFORMATION PROCESSING (GLOBALSIP) | 2016年
关键词：
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
We investigate sequence machine learning techniques on raw radio signal time-series data. By applying deep recurrent neural networks we learn to discriminate between several application layer traffic types on top of a constant envelope modulation without using an expert demodulation algorithm. We show that complex protocol sequences can be learned and used for both classification and generation tasks using this approach.
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收藏
页码：277 / 281
页数：5
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