## 1 Introduction
Semi-supervised learning mainly includes generative methods, pseudo-labeling methods, consistency regularization methods, and hybrid methods. Generation methods, such as Generative Adversarial Network (GAN) [23], Convolutional Autoencoder (CAE) [24], and diffusion models [25], because they can generate pseudo-samples that conform to the real data distribution, directly alleviating the scarcity problem of labeled data. Ref. [26] introduced a semi-supervised Auxiliary Classifier GAN (ACGAN) for modulation recognition, incorporating training tricks from Improved GAN [27] (e.g., feature matching, minibatch discrimination, and classification backbone). Both labeled and unlabeled samples were used as real samples to train the ACGAN, while labeled samples participated in the discriminator training to achieve classification capability. Ref. [28] proposed a semi-supervised Deep Convolutional GAN (DCGAN) for modulation recognition and SEI, using unlabeled samples as real samples for DCGAN training and labeled samples to train the discriminator's classification ability. Generating pseudo-labels based on high-confidence model predictions is another effective strategy to regularize deep model training. Ref. [29] proposed a Semi-Supervised SEI method using Metric Adversarial Training (MAT). Specifically, it innovatively introduced pseudo-labels into metric learning, realizing Semi-Supervised Metric Learning (SSML), and designed an objective function alternately regularized by SSML and Virtual Adversarial Training (VAT) to extract discriminative and generalizable semantic features from radio signals. Consistency regularization methods aim to maintain consistency in model outputs for perturbed inputs. [PERSON]. [30] proposed a SEI method based on Dual Consistency Regularization (DCR), combined with pseudo-labeling. It enforced consistency between the predicted class distributions of unlabeled data under different augmentations and consistency between the semantic feature distributions of labeled samples and pseudo-labeled samples to achieve more accurate emitter identification. However, semi-supervised learning still relies on pseudo-labels or generated data whose quality cannot be guaranteed. Self-Supervised Learning (SSL) is a type of unsupervised learning. It uses contrastive learning to construct auxiliary tasks, trains the RFFs extractor with unlabeled data, and then only requires a small number of labeled samples to fine-tune the RFFs extractor to complete the SEI task. Therefore, SSL is a more balanced and effective paradigm. It utilizes a large amount of unlabeled data to learn meaningful representations, while only requiring a small number of labeled samples to adapt to downstream tasks. Ref. [31] introduced an efficient self-supervised learning method referred to as BYOL [32] to SEI and designed three optimized data augmentation schemes that are phase rotation, random cropping, and jitter. Ref. [33] performed self-supervised learning of constellation trace figure to achieve feature extraction of unlabeled RF signals and SEI in the downstream task. Ref. [34] employed BYOL as the self-supervised backbone for pretraining, designed an Adversarial Augmentation (Adv-Aug) strategy, and introduced knowledge transfer to fine-tune the extractor and classifier. Ref. [35] designed an asymmetric dual-network architecture (online and target networks), employing contrastive loss to distinguish positive and negative sample pairs, combined with a non-contrastive consistency constraint for cross-network feature alignment, further enhancing the robustness and generalizability of the learned RFFs. In conclusion, both unsupervised and self-supervised learning methods can extract RFFs on unlabeled samples and serve the target SEI task. However, since the amount of labeled data available for fine-tuning is typically much smaller than the unlabeled data used in pretraining, models remain susceptible to overfitting during the fine-tuning stage.