Reliable Automatic Modulation Classification via Grayscale Spectral Quotient Constellation Matrix and Deep Learning Models

Automatic modulation classification (AMC) is one of the crucial technologies for designing an intelligent and efficient transceiver for future wireless communications. However, the channel interferences can cause instability in traditional signal representations, such as inphase and quadrature (I/Q) sequence, and constellations, leading to poor generalization and significant classification performance degradation in new channel environments. Retraining the classifier to achieve robust and effective performance in such cases requires a large number of re-collected samples and consumes vast computational resources, which makes it costly and difficult to apply in practice. To solve this problem, we propose the grayscale spectral quotient constellation matrix (GSQCM)-based AMC methods using deep learning (DL) in orthogonal frequency division multiplexing (OFDM) systems, which do not require retraining the classifier or performing equalization even for the unseen channel cases. Specifically, we first propose a novel method, named bidirectional and multi-step spectral cyclic division (BMSSCD), to generate the channel-robust spectral quotient signals in a length-extension manner. Then, we convert these generated signals into dimension-specific GSQCMs. Finally, the GSQCMs are used as the input to train our classifiers based on several classical DL models, such as AlexNet, VGGNet, GoogLeNet, and ResNet. It is noted that all of the DL-based classifiers are trained under additive white Gaussian noise (AWGN) channel but tested under Rician and Rayleigh multipath fading channels. Extensive simulations show that (i) the novel signal representation, i.e., GSQCM, is well suited as network input for the DL-based AMC methods to train the reliable classifiers, avoiding the model overfitting on the dataset collected under a specific channel condition, (ii) the proposed GSQCM-DL methods exhibit strong generalization, achieving robust and superior performance in comparison to some existing methods when the unseen propagation scenarios are considered.