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5G Signal Identification Using Deep Learning Algorithms.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	5G Signal Identification Using Deep Learning Algorithms.
作者:	Alhazmi, Mohsen H.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, 2021
面頁冊數:	81 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-02, Section: B.
附註:	Advisor: Yao, Yu-Dong.
Contained By:	Dissertations Abstracts International83-02B.
標題:	Artificial intelligence.
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28419535
ISBN:	9798534672268

5G Signal Identification Using Deep Learning Algorithms.
Alhazmi, Mohsen H.

5G Signal Identification Using Deep Learning Algorithms. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 81 p.

Source: Dissertations Abstracts International, Volume: 83-02, Section: B.

Thesis (Ph.D.)--Stevens Institute of Technology, 2021.

This item must not be sold to any third party vendors.

Spectrum awareness, including identifying different types of signals, is critical in acellular system environment. The Fifth Generation Mobile System (5G) achieves a considerable promise in terms of high data rate, low latency, and low power consumption. This work explores a neural network to identify 5G signals, among other cellular communications signals, including Long-Term Evolution (LTE) and Universal Mobile Telecommunication Service (UMTS). We investigate the use of deep learning in wireless communications systems. The signals of different cellular systems, including5G are generated to train different conventional neural networks. We consider the effects of training dataset size, features extracted, and channel fading in our study. Besides, we studied the impact of several signal-to-noise ratios. Experiment results demonstrate the effectiveness of deep learning neural networks in identifying cellular system signals, including UMTS, LTE, and 5G.This dissertation focuses on 5G signal classification using machine learning and deep learning algorithms. For deep learning algorithms approaches, convolutional neural networks are utilized. Various cellular signals were generated by using the MATLAB toolbox for UMTS, LTE, and 5G NR signals. Noise environment and interference are considered in the classification task. The actual data has been covered to test the model, including 3G, 4G, and 5G, by using Huawei's GENEX Probe. A network optimization and drive test data collection system is an air interface test tool for WCDMA/HSDPA/HSUPA/GSM/GPRS networks. Our research demonstrates the effectiveness of deep learning algorithms to identify 5G signals among various cellular signals. This work explores the features of deep learning in cellular signals identification. The cellular signals data are used to train Convolutional network (CNN) LeNet-5Based and then test those networks with various signals. We Investigate the CNN under different scenarios. Besides, the Signal-to-Noise Ratio is considered. The performance of the CNN is substantially improving when we increase the dataset size.

ISBN: 9798534672268Subjects--Topical Terms:

194058
Artificial intelligence.
Subjects--Index Terms:

Cellular communications signals

5G Signal Identification Using Deep Learning Algorithms.
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