Automatic Digital Modulation Recognition Based on Machine Learning Algorithms

Modulation type recognition has attracted increasing attention in recent years, both in the military and commercial sectors. This paper introduces new methods for the automatic identification of digital modulations. Our work targets the main type of digital modulations, including amplitude-shift keying, quadrature amplitude-shift keying, frequency-shift keying, quadrature frequency-shift keying, phase-shift keying, quadrature phase-shift keying and 16 quadrature amplitude modulation. These modulations are identified and separated by first selecting the appropriate features from the received modulated signal, and then classifying the modulation type with the help of k-nearest neighbors and probabilistic neural network algorithms. To validate our proposed methods, we perform MATLAB simulations with signal-to-noise ratio from -10 dB to 30 dB over the additive white Gaussian noise channel. The simulation results indicate that using the proposed algorithms, selecting useful features, and properly setting the tuning parameters lead to a significant improvement in accuracy and speed of modulation type recognition. Compared to existing works, the proposed algorithms achieve the highest accuracy with the least number of classifiers and adjustable parameters.