most citedDeep Learning Based MIMO Communications

141 citations · 160 across the 3 of their papers we have counts for

collaborators

7 papers

cs.IT2017141 cited

Deep Learning Based MIMO Communications

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. This metho…

cs.LG2017

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…

cs.LG201719 cited

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…

cs.LG2016

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…

cs.CR2016

A Modest Proposal for Open Market Risk Assessment to Solve the Cyber-Security Problem

Timothy J. O'Shea, Adam Mondl, T. Charles. Clancy

We introduce a model for a market based economic system of cyber-risk valuation to correct fundamental problems of incentives within the information technology and information proc…

cs.NI2016

GNU Radio Signal Processing Models for Dynamic Multi-User Burst Modems

Timothy J O'Shea, Kiran Karra

This paper presents a modern method for implementing burst modems in GNU Radio. Since burst modems are widely used for multi-user channel access and sharing in non-broadcast radio…