作者
Lauren J Wong, William H Clark, Bryse Flowers, R Michael Buehrer, William C Headley, Alan J Michaels
发表日期
2021/9/20
期刊
IEEE Open Journal of the Communications Society
卷号
2
页码范围
2243-2264
出版商
IEEE
简介
While deep learning (DL) technologies are now pervasive in state-of-the-art Computer Vision (CV) and Natural Language Processing (NLP) applications, only in recent years have these technologies started to sufficiently mature in applications related to wireless communications, a field loosely termed Radio Frequency Machine Learning (RFML). In particular, recent research has shown DL to be an enabling technology for Cognitive Radio (CR) applications as well as a useful tool for supplementing expertly defined algorithms for spectrum awareness applications such as signal detection, estimation, and classification. A major driver for the usage of RFML is that little, to no, a priori knowledge of the intended spectral environment is required, given that there is an abundance of representative raw Radio Frequency (RF) data to facilitate training and evaluation. However, in addition to this fundamental need for …
学术搜索中的文章
LJ Wong, WH Clark IV, B Flowers, RM Buehrer… - arXiv preprint arXiv:2010.00432, 2020
LJ Wong, WH Clark, B Flowers, RM Buehrer… - IEEE Open Journal of the Communications Society, 2021