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Joe H. Chow - IEEE Xplore Author Profile
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Joe H. Chow

Also published under: J. H. Chow, Joe Chow, J. Chow, Joseph Chow, J. h. Chow

Publications
272
Citations
6,531
Publications by Year
19762024
Co-Authors:
Show All Co-Authors (329)
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Joe H. Chow

Also published under: J. H. Chow, Joe Chow, J. Chow, Joseph Chow, J. h. Chow

Affiliation

Department of Electrical
Computer
and Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA

Publication Topics

Biography

Joe H. Chow (F'92) received his MS and PhD degrees from the University of Illinois, Urbana-Champaign. After working in the General Electric Power System business in Schenectady, he joined Rensselaer Polytechnic Institute in 1987. He is a professor of Electrical, Computer, and Systems Engineering and the Associate Dean of Engineering for Research and Graduate Programs. His research interests include multivariable control, power system dynamics and control, voltage-sourced converter-based FACTS Controllers, and synchronized phasor data.(Based on document published on 24 April 2009).
Publications
272
Citations
6,531
Publications by Year
19762024
Co-Authors:
Show All Co-Authors (329)
Author's Published Works

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

Cemil Vahapoglu;Timothy J. O’Shea;Tamoghna Roy;Sennur Ulukus

MILCOM 2024 - 2024 IEEE Military Communications Conference (MILCOM)
Year: 2024 | Conference Paper |
Cited by: Papers (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 coverage, low latency, and power efficiency. Yet, many of existing existing approaches remain non-practical or sub-optimal due to computational limitations, and unrealistic presumptions of static network conditions and a...Show More

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

Cemil Vahapoglu;Timothy J. O’Shea;Tamoghna Roy;Sennur Ulukus

Year: 2024 | Conference Paper |

Deep Learning Based Uplink Multi-User SIMO Beamforming Design

Cemil Vahapoglu;Timothy J. O’Shea;Tamoghna Roy;Sennur Ulukus

2024 IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN)
Year: 2024 | Conference Paper |
Cited by: Papers (2)
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. Nonetheless, traditional approaches have short-comings when it comes to computational complexity and their ability to adapt to dynamic conditions, creating a...Show More

Deep Learning Based Uplink Multi-User SIMO Beamforming Design

Cemil Vahapoglu;Timothy J. O’Shea;Tamoghna Roy;Sennur Ulukus

Year: 2024 | Conference Paper |

Detecting Irregular Network Activity with Adversarial Learning and Expert Feedback

Gopikrishna Rathinavel;Nikhil Muralidhar;Timothy O’Shea;Naren Ramakrishnan

2022 IEEE International Conference on Data Mining (ICDM)
Year: 2022 | Conference Paper |
Cited by: Papers (1)
Anomaly detection is a ubiquitous and challenging task, relevant across many disciplines. With the vital role communication networks play in our daily lives, the security of these networks is imperative for the smooth functioning of society. To this end, we propose a novel self-supervised deep learning framework CAAD for anomaly detection in wireless communication systems. Specifically, CAAD emplo...Show More

Detecting Irregular Network Activity with Adversarial Learning and Expert Feedback

Gopikrishna Rathinavel;Nikhil Muralidhar;Timothy O’Shea;Naren Ramakrishnan

Year: 2022 | Conference Paper |

SVD-Embedded Deep Autoencoder for MIMO Communications

Xinliang Zhang;Mojtaba Vaezi;Timothy J. O’Shea

ICC 2022 - IEEE International Conference on Communications
Year: 2022 | Conference Paper |
Cited by: Papers (9)
Using a deep autoencoder (DAE) for end-to-end communication in multiple-input multiple-output (MIMO) systems is a novel concept with significant potential. DAE-aided MIMO has been shown to outperform singular-value decomposition (SVD)-based precoded MIMO in terms of bit error rate (BER). This paper proposes embedding left- and right-singular vectors of the channel matrix into DAE encoder and decod...Show More

SVD-Embedded Deep Autoencoder for MIMO Communications

Xinliang Zhang;Mojtaba Vaezi;Timothy J. O’Shea

Year: 2022 | Conference Paper |

Scalable Wireless Anomaly Detection with Generative-LSTMs on RF Post-Detection Metadata

Blake Barnes-Cook;Timothy O'Shea

2022 IEEE Wireless Communications and Networking Conference (WCNC)
Year: 2022 | Conference Paper |
Cited by: Papers (2)
Signal anomaly detection is commonly used to detect rogue or unexpected signals. It has many applications in interference mitigation, wireless security, optimized spectrum allocation, and radio coordination. Our work proposes a new method for anomaly detection on signal detection metadata using generative adversarial network output processed by a long short term memory recurrent neural network. We...Show More

Scalable Wireless Anomaly Detection with Generative-LSTMs on RF Post-Detection Metadata

Blake Barnes-Cook;Timothy O'Shea

Year: 2022 | Conference Paper |

Benchmarking and Interpreting End-to-End Learning of MIMO and Multi-User Communication

Jinxiang Song;Christian Häger;Jochen Schröder;Timothy J. O’Shea;Erik Agrell;Henk Wymeersch

IEEE Transactions on Wireless Communications
Year: 2022 | Volume: 21, Issue: 9 | Journal Article |
Cited by: Papers (15)
End-to-end autoencoder (AE) learning has the potential of exceeding the performance of human-engineered transceivers and encoding schemes, without a priori knowledge of communication-theoretic principles. In this work, we aim to understand to what extent and for which scenarios this claim holds true when comparing with fair benchmarks. Our particular focus is on memoryless multiple-input multiple-...Show More

Benchmarking and Interpreting End-to-End Learning of MIMO and Multi-User Communication

Jinxiang Song;Christian Häger;Jochen Schröder;Timothy J. O’Shea;Erik Agrell;Henk Wymeersch

Year: 2022 | Volume: 21, Issue: 9 | Journal Article |

A Wideband Signal Recognition Dataset

Nathan West;Timothy O’Shea;Tamoghna Roy

2021 IEEE 22nd International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)
Year: 2021 | Conference Paper |
Cited by: Papers (11)
Signal recognition is a spectrum sensing problem that jointly requires detection, localization in time and frequency, and classification. This is a step beyond most spectrum sensing work which involves signal detection to estimate "present" or "not present" detections for either a single channel or fixed sized channels or classification which assumes a signal is present. We define the signal recog...Show More

A Wideband Signal Recognition Dataset

Nathan West;Timothy O’Shea;Tamoghna Roy

Year: 2021 | Conference Paper |

Machine learning remakes radio

Joe Downey;Ben Hilburn;Tim O'Shea;Nathan West

IEEE Spectrum
Year: 2020 | Volume: 57, Issue: 5 | Magazine Article |
Cited by: Papers (12)
THE ERA OF TELECOMMUNICAtions systems designed solely by humans is coming to an end. From here on, artificial intelligence will play a pivotal role in the design and operation of these systems. The reason is simple: rapidly escalating complexity.Show More

Machine learning remakes radio

Joe Downey;Ben Hilburn;Tim O'Shea;Nathan West

Year: 2020 | Volume: 57, Issue: 5 | Magazine Article |

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

Timothy J. O’Shea;Tamoghna Roy;Nathan West

2019 International Conference on Computing, Networking and Communications (ICNC)
Year: 2019 | Conference Paper |
Cited by: Papers (79)
Channel modeling is a critical topic when considering accurately designing or evaluating the performance of a communications system. Most prior work in designing or learning new modulation schemes has focused on using simplified analytic channel models such as additive white Gaussian noise (AWGN), Rayleigh fading channels or other similar compact parametric models. In this paper, we extend recent ...Show More

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

Timothy J. O’Shea;Tamoghna Roy;Nathan West

Year: 2019 | Conference Paper |

Demonstrating Deep Learning Based Communications Systems Over the Air In Practice

Timothy J. O’Shea;Tamoghna Roy;Nathan West;Benjamin C. Hilburn

2018 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN)
Year: 2018 | Conference Paper |
Cited by: Papers (6)
Several novel approaches to wireless communications system design have recently been introduced which use deep learning to synthesize and adapt a new class of signal processing systems to the actual data and effects present in the radio environment. Both the autoencoder-based communications system and the feature learning-based radio signal sensor represent significant progress in the ability of r...Show More

Demonstrating Deep Learning Based Communications Systems Over the Air In Practice

Timothy J. O’Shea;Tamoghna Roy;Nathan West;Benjamin C. Hilburn

Year: 2018 | Conference Paper |

Learning a Physical Layer Scheme for the MIMO Interference Channel

Tugba Erpek;Timothy J. O'Shea;T. Charles Clancy

2018 IEEE International Conference on Communications (ICC)
Year: 2018 | Conference Paper |
Cited by: Papers (33)
This paper presents a novel physical layer scheme for multiple-input multiple-output (MIMO) communication systems based on unsupervised deep learning (DL) using an autoencoder in an interference channel (IC) environment. Moreover, it extends the single-input single-output (SISO) channel autoencoder to consider fading channel conditions. In both schemes, two physical layer communication system enco...Show More

Learning a Physical Layer Scheme for the MIMO Interference Channel

Tugba Erpek;Timothy J. O'Shea;T. Charles Clancy

Year: 2018 | Conference Paper |

Over-the-Air Deep Learning Based Radio Signal Classification

Timothy James O’Shea;Tamoghna Roy;T. Charles Clancy

IEEE Journal of Selected Topics in Signal Processing
Year: 2018 | Volume: 12, Issue: 1 | Journal Article |
Cited by: Papers (1062)
We conduct an in  depth study on the performance of deep learning based radio signal classification for radio communications signals. We consider a rigorous baseline method using higher order moments and strong boosted gradient tree classification, and compare performance between the two approaches across a range of configurations and channel impairments. We consider the effects of carrier frequen...Show More

Over-the-Air Deep Learning Based Radio Signal Classification

Timothy James O’Shea;Tamoghna Roy;T. Charles Clancy

Year: 2018 | Volume: 12, Issue: 1 | Journal Article |

Learning robust general radio signal detection using computer vision methods

Tim O'Shea;Tamohgna Roy;T. Charles Clancy

2017 51st Asilomar Conference on Signals, Systems, and Computers
Year: 2017 | Conference Paper |
Cited by: Papers (34)
We introduce a new method for radio signal detection and localization within the time-frequency spectrum based on the use of convolutional neural networks for bounding box regression. Recently, this class of approach has surpassed human-level performance on computer vision benchmarks for object detection, but similar techniques have not yet been adopted for radio applications. We introduce the bas...Show More

Learning robust general radio signal detection using computer vision methods

Tim O'Shea;Tamohgna Roy;T. Charles Clancy

Year: 2017 | Conference Paper |

Physical layer deep learning of encodings for the MIMO fading channel

Timothy J. O'Shea;Tugba Erpek;T. Charles Clancy

2017 55th Annual Allerton Conference on Communication, Control, and Computing (Allerton)
Year: 2017 | Conference Paper |
Cited by: Papers (72)
We introduce a novel physical layer scheme for Multiple Input Multiple Output (MIMO) communications based on unsupervised deep learning using an autoencoder. This method extends prior work on the joint optimization of physical layer representation and encoding and decoding processes as a single end-to-end task by expanding the transmitter and receiver to the multi-antenna case. We introduce a doma...Show More

Physical layer deep learning of encodings for the MIMO fading channel

Timothy J. O'Shea;Tugba Erpek;T. Charles Clancy

Year: 2017 | Conference Paper |

Learning approximate neural estimators for wireless channel state information

Tim O'Shea;Kiran Karra;T. Charles Clancy

2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)
Year: 2017 | Conference Paper |
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 characterization from analytic system models which are used pervasively today. We explore an alternative approach to building estimators which relies principally on approximate regression using large datasets and large ...Show More

Learning approximate neural estimators for wireless channel state information

Tim O'Shea;Kiran Karra;T. Charles Clancy

Year: 2017 | Conference Paper |

Spectral detection and localization of radio events with learned convolutional neural features

Timothy J. O'Shea;Tamoghna Roy;Tugba Erpek

2017 25th European Signal Processing Conference (EUSIPCO)
Year: 2017 | Conference Paper |
We introduce a method for detecting, localizing and identifying radio transmissions within wide-band time-frequency power spectrograms using feature learning using convolutional neural networks on their 2D image representation. By doing so we build a foundation for higher level contextual radio spectrum event understanding, labeling, and reasoning in complex shared spectrum and many-user environme...Show More

Spectral detection and localization of radio events with learned convolutional neural features

Timothy J. O'Shea;Tamoghna Roy;Tugba Erpek

Year: 2017 | Conference Paper |

An Introduction to Deep Learning for the Physical Layer

Timothy O’Shea;Jakob Hoydis

IEEE Transactions on Cognitive Communications and Networking
Year: 2017 | Volume: 3, Issue: 4 | Journal Article |
Cited by: Papers (2103)
We present and discuss several novel applications of deep learning for the physical layer. By interpreting a communications system as an autoencoder, we develop a fundamental new way to think about communications system design as an end-to-end reconstruction task that seeks to jointly optimize transmitter and receiver components in a single process. We show how this idea can be extended to network...Show More

An Introduction to Deep Learning for the Physical Layer

Timothy O’Shea;Jakob Hoydis

Year: 2017 | Volume: 3, Issue: 4 | Journal Article |

Deep architectures for modulation recognition

Nathan E West;Tim O'Shea

2017 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN)
Year: 2017 | Conference Paper |
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 ratio modulation recognition is not limited by network depth and further work should focus on improving learned synchronization and equalization. Advances in these areas will likely come from novel architectures designed for these tasks or thro...Show More

Deep architectures for modulation recognition

Nathan E West;Tim O'Shea

Year: 2017 | Conference Paper |

Semi-supervised radio signal identification

Timothy J. O'Shea;Nathan West;Matthew Vondal;T. Charles Clancy

2017 19th International Conference on Advanced Communication Technology (ICACT)
Year: 2017 | Conference Paper |
Cited by: Papers (57)
Radio emitter recognition in dense multi-user environments is an important tool for optimizing spectrum utilization, identifying and minimizing interference, and enforcing spectrum policy. Radio data is readily available and easy to obtain from an antenna, but labeled and curated data is often scarce making supervised learning strategies difficult and time consuming in practice. We demonstrate tha...Show More

Semi-supervised radio signal identification

Timothy J. O'Shea;Nathan West;Matthew Vondal;T. Charles Clancy

Year: 2017 | Conference Paper |

End-to-end radio traffic sequence recognition with recurrent neural networks

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

2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP)
Year: 2016 | Conference Paper |
Cited by: Papers (30)
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 application layer traffic types on top of a constant envelope modulation without using an expert demodulation algorithm. We show that complex protocol sequences can be learned and used for both classification and generation ta...Show More

End-to-end radio traffic sequence recognition with recurrent neural networks

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

Year: 2016 | Conference Paper |

Learning to communicate: Channel auto-encoders, domain specific regularizers, and attention

Timothy J. O'Shea;Kiran Karra;T. Charles Clancy

2016 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)
Year: 2016 | Conference Paper |
We address the problem of learning an efficient and adaptive physical layer encoding to communicate binary information over an impaired channel. In contrast to traditional work, we treat the problem an unsupervised machine learning problem focusing on optimizing reconstruction loss through artificial impairment layers in an autoencoder (we term this a channel autoencoder) and introduce several new...Show More

Learning to communicate: Channel auto-encoders, domain specific regularizers, and attention

Timothy J. O'Shea;Kiran Karra;T. Charles Clancy

Year: 2016 | Conference Paper |

Radio transformer networks: Attention models for learning to synchronize in wireless systems

Timothy J. O'Shea;Latha Pemula;Dhruv Batra;T. Charles Clancy

2016 50th Asilomar Conference on Signals, Systems and Computers
Year: 2016 | Conference Paper |
We introduce learned attention models into the radio machine learning domain for the task of modulation recognition by leveraging spatial transformer networks and introducing new radio domain appropriate transformations. 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 signal's structu...Show More

Radio transformer networks: Attention models for learning to synchronize in wireless systems

Timothy J. O'Shea;Latha Pemula;Dhruv Batra;T. Charles Clancy

Year: 2016 | Conference Paper |

Unsupervised representation learning of structured radio communication signals

Timothy J. O'Shea;Johnathan Corgan;T. Charles Clancy

2016 First International Workshop on Sensing, Processing and Learning for Intelligent Machines (SPLINE)
Year: 2016 | Conference Paper |
We explore unsupervised representation learning of radio communication signals in raw sampled time series representation. We demonstrate that we can learn modulation basis functions using convolutional autoencoders and visually recognize their relationship to the analytic bases used in digital communications. We also propose and evaluate quantitative metrics for quality of encoding using domain re...Show More

Unsupervised representation learning of structured radio communication signals

Timothy J. O'Shea;Johnathan Corgan;T. Charles Clancy

Year: 2016 | Conference Paper |

Demonstrated LLC-layer attack and defense strategies for wireless communication systems

S. Hitefield;V. Nguyen;C. Carlson;T. O'Shea;T. Clancy

2014 IEEE Conference on Communications and Network Security
Year: 2014 | Conference Paper |
In this work we demonstrate an over-the-air capability to exploit software weaknesses in the signal processing code implementing the physical and link layers of the OSI stack. Our test bed includes multiple nodes leveraging both GNU Radio and the Universal Software Radio Peripheral to demonstrate these attacks and corresponding defensive strategies. More specifically, we examine two duplex modem i...Show More

Demonstrated LLC-layer attack and defense strategies for wireless communication systems

S. Hitefield;V. Nguyen;C. Carlson;T. O'Shea;T. Clancy

Year: 2014 | Conference Paper |

Measuring smart jammer strategy efficacy over the air

C. Carlson;V. Nguyen;S. Hitefield;T. O'Shea;T. Clancy

2014 IEEE Conference on Communications and Network Security
Year: 2014 | Conference Paper |
Recently several information theoretic communications investigations have focused on developing efficient ways to disrupt various wireless communications protocols. These include targeted jamming waveforms that intentionally target algorithms in the low layer PHY and MAC layer, which are more susceptible to certain observations than traditional jamming waveforms. In this effort we seek to demonstr...Show More

Measuring smart jammer strategy efficacy over the air

C. Carlson;V. Nguyen;S. Hitefield;T. O'Shea;T. Clancy

Year: 2014 | Conference Paper |

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