Identification of Cellular Signal Measurements Using Machine Learning

• 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
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2023.3238695

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%.