๐ฎ
๐ฎ
The Ethereal
End-to-End Radio Traffic Sequence Recognition with Deep Recurrent Neural Networks
October 03, 2016 ยท Declared Dead ยท ๐ IEEE Global Conference on Signal and Information Processing
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Timothy J. O'Shea, Seth Hitefield, Johnathan Corgan
arXiv ID
1610.00564
Category
cs.LG: Machine Learning
Cross-listed
cs.NI
Citations
47
Venue
IEEE Global Conference on Signal and Information Processing
Last Checked
6 months ago
Abstract
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.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
๐ Similar Papers
In the same crypt โ Machine Learning
๐ฎ
๐ฎ
The Ethereal
Continuous control with deep reinforcement learning
๐
๐
Old Age
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
๐
๐
Old Age
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
๐
๐
Old Age
SGDR: Stochastic Gradient Descent with Warm Restarts
๐ฎ
๐ฎ
The Ethereal
Asynchronous Methods for Deep Reinforcement Learning
Died the same way โ ๐ป Ghosted
R.I.P.
๐ป
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
๐ป
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
๐ป
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
๐ป
Ghosted