Carrier Frequency Offset Robust Self-Compensating Neural Network

Automatic modulation recognition plays a critical role in modern communication systems as it provides valuable information for tasks such as signal recognition, interference jamming, and interception. Despite the significant potential of machine learning in this field, its practical effectiveness often falls short of expectations. In non-cooperative communication systems, irregular phase shifts and carrier frequency offsets (CFO) in the signal can negatively impact recognition accuracy. Conventional algorithms for compensating CFO, including those reliant on phase-locked loops, as well as various existing blind modulation techniques, encounter constraints in non-cooperative environments. This paper presents a novel CFO self-compensating neural network. A CFO compensation module is designed and integrated into the neural network. Additionally, a regularization term is proposed to evaluate the effectiveness of CFO compensation, which is incorporated into the loss function. The effectiveness of the proposed approach is demonstrated through experiments conducted on both publicly accessible datasets and experimentally measured data generated using software-defined radio. Experimental results demonstrate that the proposed new architecture maintains reliable recognition capabilities even in the presence of CFO. Additionally, it can be applied to all modulation schemes that are demodulable using constellation diagrams in non-cooperative conditions.