Deep Learning Classification of 3.5 GHz Band Spectrograms with Applications to Spectrum Sensing.
- DOI
- 10.1109/tccn.2019.2899871
- Published
- 2019-01-01
- Container
- IEEE Trans Cogn Commun Netw
- Publisher
- Not recorded
- Open access
- no
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Cite this work
BibTeX
@article{allodium:10.1109/tccn.2019.2899871,
title = {Deep Learning Classification of 3.5 GHz Band Spectrograms with Applications to Spectrum Sensing.},
author = {Lees WM and Wunderlich A and Jeavons P and Hale PD and Souryal MR.},
year = {2019},
journal = {IEEE Trans Cogn Commun Netw},
doi = {10.1109/tccn.2019.2899871},
url = {https://doi.org/10.1109/tccn.2019.2899871}
}RIS
TY - JOUR TI - Deep Learning Classification of 3.5 GHz Band Spectrograms with Applications to Spectrum Sensing. AU - Lees WM AU - Wunderlich A AU - Jeavons P AU - Hale PD AU - Souryal MR. PY - 2019 JO - IEEE Trans Cogn Commun Netw DO - 10.1109/tccn.2019.2899871 UR - https://doi.org/10.1109/tccn.2019.2899871 ER -
APA
WM, L., A, W., P, J., PD, H., & MR., S. (2019). Deep Learning Classification of 3.5 GHz Band Spectrograms with Applications to Spectrum Sensing.. IEEE Trans Cogn Commun Netw. https://doi.org/10.1109/tccn.2019.2899871
Source records
- europe-pmc · retrieved 2026-11-10T14:12:09.272Z