## 2 Related Works
### DSSL Methods for Indoor Positioning  
In recent years, deep learning algorithms have shown great potential in solving complex positioning problems, and DSSL methods have been emerging as a promising approach to deal with the challenges of limited labeled data in positioning problems by leveraging both labeled and unlabeled data to train deep learning models. The branches of DSSL mainly include pseudo-label methods [39], deep generative methods [40], graph-based methods [41], consistency regularize methods [15; 16] and hybrid methods [42]. Currently, research on DSSL mainly focuses on the image classification task. For DSSL methods applied in image classification tasks, each branch has advanced algorithms capable of achieving high accuracy. Regarding the pseudo-labeling method, using the high-confidence model's predictions as pseudo labels for unlabeled samples is a common approach known as self-training. [PERSON]. [39] proposed a simple and efficient training framework for neural networks. The model is first trained in a usual supervised manner; this trained model is used to attain predictions from unlabeled data. The crossentropy loss is used in the process of obtaining predictions from unlabeled samples, and when we obtain soft labels, the model's highest confidence predictions are viewed as pseudo labels. Consistency regularization is a technique that typically involves using a single model to make multiple predictions with different input noise or model parameters each time. It aims to obtain a similar prediction result under different noisy inputs and parameters, thereby improving the generalization of the model. From certain perspectives, using consistency regularization can also be observed as generating pseudo-labels. However, it focuses on obtaining accurate labels by regularizing the distance between outputs and supervised training, which is fundamentally different from the pseudo-labeling method. [PERSON]. [15] proposed two training frameworks, named II Model and Temporal Ensembling, respectively. During each epoch of training with the II Model, the same batch of unlabeled samples is processed by the same model twice after adding random perturbations. Some inconsistencies in the two predictions will exist because of the different perturbations, so the II Model uses a consistency loss function to minimize the disparity between the two predicted outputs. Temporal Ensembling makes some improvements to the II Model. Due to the fact that the II Model requires two rounds of inference at each step, it slows down the inference speed. To overcome this defect, the Temporal Ensembling model only needs one round of inference, while one prediction is obtained by calculating the moving average of a historical output over a certain period of time. Another prediction is generated by the current output. By combining the loss items of two predictions as a loss function, the inconsistency in the two predictions decreases. Mean Teacher [16] is an improved method for Temporal Ensembling, and it consists of a teacher model and a student model. The student model resembles the II Model, while the teacher model shares the same structure as the student model but incorporates Exponential Moving Average (EMA) of the student weights. This allows Mean Teacher to enforce a consistency constraint between the predictions of the student and teacher models. Results from [16] indicate that Mean Teacher performs superiorly in test accuracy compared to Temporal Ensembling, while it also allows for training with fewer labels. DSSL-based indoor positioning is often considered as a regression problem rather than a classification problem, and previous research on DSSL for indoor positioning mainly focuses on pseudo-label methods [43], deep generative methods [44], and graph-based methods [45]. The idea of the pseudo-label method in [43] is to pretrain the initial model with labeled data and use the trained model to predict unlabeled data, treating the predictions as pseudo-labels.