141 citations · 166 across the 8 of their papers we have counts for
5 papers · 1 filter
Approximating the Void: Learning Stochastic Channel Models from Observation with Variational Generative Adversarial Networks
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. Most prior work in designing or learning new…
Learning Approximate Neural Estimators for Wireless Channel State Information
Timothy J. O'Shea, Kiran Karra, T. Charles Clancy
Estimation is a critical component of synchronization in wireless and signal processing systems. There is a rich body of work on estimator derivation, optimization, and statistical…
Deep Architectures for Modulation Recognition
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. Results show that radio modulation recogni…
Deep Reinforcement Learning Radio Control and Signal Detection with KeRLym, a Gym RL Agent
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…
Unsupervised Representation Learning of Structured Radio Communication Signals
Timothy J. O'Shea, Johnathan Corgan, T. Charles Clancy
We explore unsupervised representation learning of radio communication signals in raw sampled time series representation. We demonstrate that we can learn modulation basis function…