Search Results for author: Timothy J. O'Shea

Found 18 papers, 6 papers with code

Deep Learning Based Uplink Multi-User SIMO Beamforming Design

no code implementations28 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.


SVD-Embedded Deep Autoencoder for MIMO Communications

no code implementations3 Nov 2021 Xinliang Zhang, Mojtaba Vaezi, Timothy J. O'Shea

SVDembedded DAE largely outperforms theoretic linear precoding in terms of BER.


Deep Learning for Wireless Communications

no code implementations12 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.

Approximating the Void: Learning Stochastic Channel Models from Observation with Variational Generative Adversarial Networks

no code implementations16 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.

Physical Layer Communications System Design Over-the-Air Using Adversarial Networks

no code implementations8 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.

Over the Air Deep Learning Based Radio Signal Classification

5 code implementations13 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.

General Classification

Deep Learning Based MIMO Communications

no code implementations25 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

Learning Approximate Neural Estimators for Wireless Channel State Information

no code implementations19 Jul 2017 Timothy J. O'Shea, Kiran Karra, T. Charles Clancy

Estimation is a critical component of synchronization in wireless and signal processing systems.

Deep Architectures for Modulation Recognition

no code implementations27 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.

BIG-bench Machine Learning

An Introduction to Deep Learning for the Physical Layer

1 code implementation2 Feb 2017 Timothy J. O'Shea, Jakob Hoydis

We present and discuss several novel applications of deep learning for the physical layer.

General Classification

Recurrent Neural Radio Anomaly Detection

no code implementations1 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.

Anomaly Detection Novelty Detection

Semi-Supervised Radio Signal Identification

1 code implementation1 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.


Learning to Communicate: Channel Auto-encoders, Domain Specific Regularizers, and Attention

no code implementations23 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.

Deep Reinforcement Learning Radio Control and Signal Detection with KeRLym, a Gym RL Agent

1 code implementation30 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.

reinforcement-learning Reinforcement Learning (RL)

Radio Transformer Networks: Attention Models for Learning to Synchronize in Wireless Systems

no code implementations3 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.

General Classification

Unsupervised Representation Learning of Structured Radio Communication Signals

1 code implementation24 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.

Representation Learning Time Series +1

Convolutional Radio Modulation Recognition Networks

8 code implementations12 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.

General Classification Time Series +1

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