Cellular Signal Identification Using Convolutional Neural Networks: AWGN and Rayleigh Fading Channels

Abstract

Spectrum awareness is crucial in wireless communications systems for dynamic network environments. It is required for spectrum resource management, adaptive transmissions, and interference detection. Existing spectrum awareness research includes tasks of spectrum sensing, modulation classification, and medium access control protocol (MAC) identification. This paper explores the identification and classification of signals of various cellular networks, including Global System for Mobile (GSM), Universal Mobile Telecommunication Service (UMTS), and Long-Term Evolution (LTE). We utilize deep learning, specifically, convolutional neural networks (CNN), in training and testing wireless fading signals in those cellular networks. Experimentations demonstrate the effectiveness of deep learning in cellular signal identification.