5G Signal Identification Using Deep Learning

Mohsen H. Alhazmi, Mofadal Alymani, Hatim Alhazmi, Alhussain Almarhabi, Abdullah Samarkandi, Yu Dong Yao

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

25 Scopus citations

Abstract

Spectrum awareness, including identifying different types of signals, is very important in a cellular system environment. In this paper, a neural network is utilized to identify 5G signals among different cellular communications signals, including Long-Term Evolution (LTE) and Universal Mobile Telecommunication Service (UMTS). We explore the use of deep learning in wireless communications systems. We consider the effects of training dataset size, features extracted, and channel fading in our study. Experiment results demonstrate the effectiveness of deep learning neural networks in identifying cellular system signals, including UMTS, LTE, and 5G.

Original languageEnglish
Title of host publication2020 29th Wireless and Optical Communications Conference, WOCC 2020
ISBN (Electronic)9781728161242
DOIs
StatePublished - May 2020
Event29th Wireless and Optical Communications Conference, WOCC 2020 - Newark, United States
Duration: 1 May 20202 May 2020

Publication series

Name2020 29th Wireless and Optical Communications Conference, WOCC 2020

Conference

Conference29th Wireless and Optical Communications Conference, WOCC 2020
Country/TerritoryUnited States
CityNewark
Period1/05/202/05/20

Keywords

  • Classification
  • Convolutional Neural Network (CNN)
  • Deep Learning (DL)
  • Fifth Generation New Radio (5G)
  • Long-Term Evolution (LTE)
  • Machine learning (ML)
  • Rayleigh Fading
  • Universal Mobile Telecommunication Service (UMTS)

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