activity
20162024
most citedRecurrent Neural Radio Anomaly Detection

45 citations · 48 across the 5 of their papers we have counts for

collaborators

5 papers

cs.IT2024

Deep Learning Based Joint Multi-User MISO Power Allocation and Beamforming Design

Cemil Vahapoglu, Timothy J. O'Shea, Tamoghna Roy +1

The evolution of fifth generation (5G) wireless communication networks has led to an increased need for wireless resource management solutions that provide higher data rates, wide…

cs.IT2023

Deep Learning Based Uplink Multi-User SIMO Beamforming Design

Cemil Vahapoglu, Timothy J. O'Shea, Tamoghna Roy +1

The advancement of fifth generation (5G) wireless communication networks has created a greater demand for wireless resource management solutions that offer high data rates, extensi…

cs.LG201645 cited

Recurrent Neural Radio Anomaly Detection

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. This approach holds promise in significantly in…

cs.LG20163 cited

End-to-End Radio Traffic Sequence Recognition with Deep Recurrent Neural Networks

Timothy J. O'Shea, Seth Hitefield, Johnathan Corgan

We investigate sequence machine learning techniques on raw radio signal time-series data. By applying deep recurrent neural networks we learn to discriminate between several applic…

cs.LG2016

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

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. We treat the problem as reconstruction optimization throu…