Identification of Cellular Signal Measurements Using Machine Learning

Spectrum awareness has a plethora of civilian and defense applications, such as spectrum resource management, adaptive transmissions, interference detection, and identification of threat signals. This article proposes an identification neural network (INN)-based model that identifies cellular signal...

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Bibliographic Details
Published in:IEEE transactions on instrumentation and measurement 2023, Vol.72, p.1-4
Main Authors: Makled, Esraa A., Al-Nahhal, Ibrahim, Dobre, Octavia A., Ureten, Oktay
Format: Article
Language:English
Subjects:
3G mobile communication
Artificial neural networks
Cellular communication
Communications systems
Computational modeling
False alarms
Frequency measurement
GSM
Long Term Evolution
Machine learning
Military applications
Neural networks
Neural networks (NNs)
Numerical models
over-the-air data
Power spectral density
practical cellular measurements
Resource management
Wideband
wideband signal identification
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Summary:Spectrum awareness has a plethora of civilian and defense applications, such as spectrum resource management, adaptive transmissions, interference detection, and identification of threat signals. This article proposes an identification neural network (INN)-based model that identifies cellular signals from three different radio access technologies, namely global system for mobile (GSM) communications, universal mobile telecommunications service, and long-term evolution. The proposed INN identifies whether or not the measured power spectral density belongs to a certain cellular signal type. Two data collection approaches (DCAs) are considered: in-band and multiple-band. The over-the-air measurements for the two DCAs show that with low computational complexity, the proposed INN model provides an identification accuracy between 93% and 100%, with a false alarm (FA) rate between 0% and 10%.
ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2023.3238695