Machine Learning Student Seminar-High-Performance Deep Learning Classification for Radio Signals

Title: High-Performance Deep Learning Classification for Radio Signals
Speaker: William Haftel
Date and Time:  Friday, February 14, 2:00PM-3:00PM
Place: Phillips Hall (801 22nd Street), Room 736
 

Abstract:  The ability to classify different types of signal modulations in radio transmissions is an important task with applications in defense, networking, and communications. This process
has traditionally been done manually by human analysts. Recent advances have shown that applying deep learning methods to this task is feasible. But existing recognition networks are complex, with heavy computational requirements, and poor accuracy on some modulation types and in noisy environments. We have built a robust radio frequency signal classifier with a hybrid approach that uses images derived from signal constellation and spectrogram data, combined with an efficient convolutional neural network. Compared to the state-of-the-art deep learning classifier, our system obtains better accuracy, with lower computational requirements.

Short Bio: Will Haftel is an undergraduate senior studying computer engineering and has been doing machine learning research for the past year and a half.  He is currently working on studying the mathematics behind machine learning in order to have a more thorough mathematical justification for this research.  He is also looking to continue research into graduate studies in the future. This talk is based on research conducted under the supervision of Professor Howie Huang. The research group also includes PhD student Ahsen Uppal..