Radio Machine Learning Dataset Generation with GNU Radio

  • Timothy J O'Shea Virginia Tech
  • Nathan West Oklahoma State

Abstract

This paper surveys emerging applications of Machine Learning (ML) to the Radio Signal Processing domain.  Provides some brief background on enabling methods and discusses some of the potential advancements for the field.  It discusses the critical importance of good datasets for model learning, testing, and evaluation and introduces several public open source synthetic datasets for various radio machine learning tasks.  These are intended to provide a robust common baselines for those working in the field and to provide a benchmark measure against which many techniques can be rapidly evaluated and compared.

Published
Sep 6, 2016
How to Cite
O'SHEA, Timothy J; WEST, Nathan. Radio Machine Learning Dataset Generation with GNU Radio. Proceedings of the GNU Radio Conference, [S.l.], v. 1, n. 1, sep. 2016. Available at: <http://pubs.gnuradio.org/index.php/grcon/article/view/11>. Date accessed: 26 july 2017.