Automatic Modulation Classification for NLOS 5G Signals with Deep Learning Approaches

The Automatic Modulation Classification (AMC) technique identifies the modulation scheme used in a received signal automatically without any prior knowledge or manual intervention. When multiple signals with different modulation schemes coexist in wireless communication systems, it’s a crucial task. In AMC, features and patterns are typically analyzed to classify signals into one of several predefined modulation schemes based on machine learning algorithms. It has been shown that deep learning (DL) algorithms are effective in AMC. Using deep neural networks for modulation classification is an excellent choice since they can learn complex patterns and hierarchical representations from data. In this paper, first we produce a dataset for non-line-of-sight signals with digital modulation of Binary Phase-shift keying (BPSK), Quadrature Phase Shift Keying (QPSK), and Quadrature Amplitude Modulation (QAM) which are widely used in modern telecommunications to transmit information. We also use three popular DL approaches in many applications for classification and recognition based on DNN, CNN and LSTM for classification. The experimental results show that the CNN approach outperforms the others significantly.