no code implementations • 12 May 2020 • Tugba Erpek, Timothy J. O'Shea, Yalin E. Sagduyu, Yi Shi, T. Charles Clancy
Existing communication systems exhibit inherent limitations in translating theory to practice when handling the complexity of optimization for emerging wireless applications with high degrees of freedom.
5 code implementations • 13 Dec 2017 • Timothy J. O'Shea, Tamoghna Roy, T. Charles Clancy
We conduct an in depth study on the performance of deep learning based radio signal classification for radio communications signals.
no code implementations • 25 Jul 2017 • Timothy J. O'Shea, Tugba Erpek, T. Charles Clancy
We introduce a novel physical layer scheme for single user Multiple-Input Multiple-Output (MIMO) communications based on unsupervised deep learning using an autoencoder.
Information Theory Information Theory
no code implementations • 19 Jul 2017 • Timothy J. O'Shea, Kiran Karra, T. Charles Clancy
Estimation is a critical component of synchronization in wireless and signal processing systems.
1 code implementation • 9 Nov 2016 • Amr S. Abed, T. Charles Clancy, David S. Levy
In this paper, we present the results of using bags of system calls for learning the behavior of Linux containers for use in anomaly-detection based intrusion detection system.
Cryptography and Security
1 code implementation • 1 Nov 2016 • Timothy J. O'Shea, Nathan West, Matthew Vondal, T. Charles Clancy
Radio emitter recognition in dense multi-user environments is an important tool for optimizing spectrum utilization, identifying and minimizing interference, and enforcing spectrum policy.
no code implementations • 1 Nov 2016 • Timothy J. O'Shea, T. Charles Clancy, Robert W. McGwier
We introduce a powerful recurrent neural network based method for novelty detection to the application of detecting radio anomalies.
no code implementations • 23 Aug 2016 • Timothy J. O'Shea, Kiran Karra, T. Charles Clancy
We address the problem of learning efficient and adaptive ways to communicate binary information over an impaired channel.
1 code implementation • 30 May 2016 • Timothy J. O'Shea, T. Charles Clancy
This paper presents research in progress investigating the viability and adaptation of reinforcement learning using deep neural network based function approximation for the task of radio control and signal detection in the wireless domain.
no code implementations • 3 May 2016 • Timothy J. O'Shea, Latha Pemula, Dhruv Batra, T. Charles Clancy
This attention model allows the network to learn a localization network capable of synchronizing and normalizing a radio signal blindly with zero knowledge of the signals structure based on optimization of the network for classification accuracy, sparse representation, and regularization.
1 code implementation • 24 Apr 2016 • Timothy J. O'Shea, Johnathan Corgan, T. Charles Clancy
We explore unsupervised representation learning of radio communication signals in raw sampled time series representation.
8 code implementations • 12 Feb 2016 • Timothy J. O'Shea, Johnathan Corgan, T. Charles Clancy
We study the adaptation of convolutional neural networks to the complex temporal radio signal domain.