everal existing works investigate the combination of DL with automatic modulation recog- nition in CRNs. In [130], a DL-based automated modulation classification method that employed Spectral Correlation Function (SCF) was proposed. Deep Belief Network (DBN) was applied for pat- tern recognition and classification. By using noise-resilient SCF signatures and DBN that are effective in learning complex patterns, the proposed method can achieve high accuracy in modulation detection and classification even in the presence of environmental noises. The efficiency of the proposed method was verified in classifying 4FSK, 16QAM, BPSK, QPSK, and OFDM modulation based on various environments settings. ML provides effective tools for automating CR functionalities by reliably extracting and learning intrinsic spectrum dynamics. However, there are two critical challenges. First, ML requires a significant amount of training data to capture complex channel and emitter characteristics and train the algorithm of classifiers. Second, the training data that has been identified for one spectrum environment cannot be used for another, especially when channel and emitter conditions change [39]. To address these challenges, various robust spectrum sensing mechanisms have been developed. A new approach to training data augmentation and domain adaptation was presented in [39]. A Generative Adversarial Network (GAN) with DL structures was employed to generate additional synthetic training data to improve classifier accuracy and adapt training data to spectrum dynamics. This approach can be used to perform spectrum sensing when only limited training data is avail- able and no knowledge of spectrum statistics is assumed. Another robust spectrum sensing framework based on DL was proposed in [148]. The received signals at the SU's receiver were filtered, sampled, and then directly fed into a convolutional neural network (CNN). To improve the adaptive ability of the classifier, Transfer Learning (TL) was incorporated into the framework to impro