TY - GEN
T1 - Combined Signal Representations for Modulation Classification Using Deep Learning
T2 - 32nd Wireless and Optical Communications Conference, WOCC 2023
AU - Samarkandi, Abdullah
AU - Almarhabi, Alhussain
AU - Alhazmi, Hatim
AU - Yao, Yu Dong
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - We exploit deep learning convolutional neural networks (CNN) based on joint image representation and propose an automatic modulation classification algorithm to classify the communication signals. The combined representations include a constellation diagram, an ambiguity function (AF), and an eye diagram. Experimentation results show that combining constellation and eye diagrams achieves superior classification performance compared to having these representations separately. Combining AF and an eye diagram results in improvement at low SNR.
AB - We exploit deep learning convolutional neural networks (CNN) based on joint image representation and propose an automatic modulation classification algorithm to classify the communication signals. The combined representations include a constellation diagram, an ambiguity function (AF), and an eye diagram. Experimentation results show that combining constellation and eye diagrams achieves superior classification performance compared to having these representations separately. Combining AF and an eye diagram results in improvement at low SNR.
KW - Spectrum awareness
KW - ambiguity function
KW - constellation diagram
KW - deep learning
KW - eye diagram
KW - modulation classification
KW - quadrature amplitude modulation (QAM)
UR - http://www.scopus.com/inward/record.url?scp=85162686756&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85162686756&partnerID=8YFLogxK
U2 - 10.1109/WOCC58016.2023.10139474
DO - 10.1109/WOCC58016.2023.10139474
M3 - Conference contribution
AN - SCOPUS:85162686756
T3 - 32nd Wireless and Optical Communications Conference, WOCC 2023
BT - 32nd Wireless and Optical Communications Conference, WOCC 2023
Y2 - 5 May 2023 through 6 May 2023
ER -