Automatic Modulation Classification Using Contrastive Fully Convolutional Network

Automatic modulation classification (AMC) aims at identifying the modulation format of the received signal. In this letter, we propose a novel grid constellation matrix (GCM)-based AMC method using a contrastive fully convolutional network (CFCN). We use GCMs as the input of the network, which are extracted from the received signals using low-complexity preprocessing. Moreover, a loss function with contrastive loss is designed to train the CFCN, which boosts the discrepancies among different modulations and obtains discriminative representations. Extensive simulations demonstrate that CFCN performs superior classification performance and better robustness to model mismatches with low training time comparing with other recent methods.