A Generative Self-Supervised Framework for Cognitive Radio Leveraging Time-Frequency Features and Attention-Based Fusion

Abstract

With the advancement of cognitive radio technology (CRT) in radio communication networks, deep learning (DL) has become instrumental in enhancing spectrum efficiency. However, supervised DL methods demand extensive labeled data and incur high manual costs. Consequently, practical applications of CRT increasingly necessitate techniques capable of learning robust representations from large volumes of unlabeled data. Although recent DL advancements have driven the use of self-supervised learning (SSL) in CRT through time-domain contrastive methods, these approaches fall short in extracting high-level spectral representations due to their neglect of time-frequency features. To address these limitations, a generative SSL framework is proposed for CRT applications. First, SSL pretraining is conducted in the time-frequency domain by reconstructing masked spectrograms using a Masked Autoencoder. Then, to recover the spectrogram under extreme radio conditions, mutual information maximization is employed to extract high-level spectral information obscured by noise patterns. Additionally, an attention-based channel-spectrum fusion module is designed to automatically extract and integrate features from the channel and spectral domains. The feasibility of the proposed framework is evaluated across multiple downstream tasks on four public datasets. Experimental results demonstrate that the proposed framework significantly outperforms existing methods in various downstream tasks.