no code implementations • 28 Sep 2023 • Cemil Vahapoglu, Timothy J. O'Shea, Tamoghna Roy, Sennur Ulukus
The advancement of fifth generation (5G) wireless communication networks has created a greater demand for wireless resource management solutions that offer high data rates, extensive coverage, minimal latency and energy-efficient performance.
no code implementations • 3 Nov 2021 • Xinliang Zhang, Mojtaba Vaezi, Timothy J. O'Shea
SVDembedded DAE largely outperforms theoretic linear precoding in terms of BER.
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.
no code implementations • 16 May 2018 • Timothy J. O'Shea, Tamoghna Roy, Nathan West
Channel modeling is a critical topic when considering designing, learning, or evaluating the performance of any communications system.
no code implementations • 8 Mar 2018 • Timothy J. O'Shea, Tamoghna Roy, Nathan West, Benjamin C. Hilburn
This paper presents a novel method for synthesizing new physical layer modulation and coding schemes for communications systems using a learning-based approach which does not require an analytic model of the impairments in the channel.
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.
no code implementations • 27 Mar 2017 • Nathan E West, Timothy J. O'Shea
We survey the latest advances in machine learning with deep neural networks by applying them to the task of radio modulation recognition.
1 code implementation • 2 Feb 2017 • Timothy J. O'Shea, Jakob Hoydis
We present and discuss several novel applications of deep learning for the physical layer.
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 • 3 Oct 2016 • Timothy J. O'Shea, Seth Hitefield, Johnathan Corgan
We investigate sequence machine learning techniques on raw radio signal time-series data.
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.