TY - GEN
T1 - Cellular system identification using deep learning
T2 - 28th Wireless and Optical Communications Conference, WOCC 2019
AU - Alshathri, Khalid
AU - Xia, Hongtao
AU - Lawrence, Victor
AU - Yao, Yu Dong
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/5
Y1 - 2019/5
N2 - Deep learning (DL) is an effective tool in artificial intelligence (AI), especially in image based and human behavior recognition applications. However, there are many applications that are not very well explored using the DL tools. The telecommunications and networking applications are among those applications that can be explored more extensively using DL. In this paper, the neural network is utilized to identify different cellular communications signals including GSM, UMTS, and LTE. Our study results show that the cellular system identification method achieves very good identification performance without any necessity to select signal features manually.
AB - Deep learning (DL) is an effective tool in artificial intelligence (AI), especially in image based and human behavior recognition applications. However, there are many applications that are not very well explored using the DL tools. The telecommunications and networking applications are among those applications that can be explored more extensively using DL. In this paper, the neural network is utilized to identify different cellular communications signals including GSM, UMTS, and LTE. Our study results show that the cellular system identification method achieves very good identification performance without any necessity to select signal features manually.
UR - http://www.scopus.com/inward/record.url?scp=85070378928&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85070378928&partnerID=8YFLogxK
U2 - 10.1109/WOCC.2019.8770700
DO - 10.1109/WOCC.2019.8770700
M3 - Conference contribution
AN - SCOPUS:85070378928
T3 - 2019 28th Wireless and Optical Communications Conference, WOCC 2019 - Proceedings
BT - 2019 28th Wireless and Optical Communications Conference, WOCC 2019 - Proceedings
Y2 - 9 May 2019 through 10 May 2019
ER -