Automatic Modulation Classification in Cognitive-IoT Radios Using Generalized Dynamic Bayesian Networks

This paper proposes a novel Automatic Modulation Classification (AMC) method for CR-IoT based on learning multiple Generalized Dynamic Bayesian Networks (GDBN) as representations of various signals under different modulation schemes. The CR-IoT performs multiple predictions online in parallel and evaluates multiple abnormality measurements based on a Modified Markov Jump Particle Filter (M-MJPF) to select the best model that explains the received signal and recognize the modulation scheme accordingly. The simulated results based on a real dataset demonstrate that the proposed GDBN-based AMC method outperforms both Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) in terms of classification accuracy.