## 1 Introduction
Then, based on certain criteria, the first path of arrival is determined from the CIR, and the phase is calculated to obtain the carrier phase measurement value. Therefore, the CIR signal can be considered as the original information of the carrier phase to distinguish multipath characteristics and can potentially be used for accurate and pervasive indoor positioning [9]. In addition, CSI can be also obtained from CFR, which is the sampled version of CFR at the granularity of the subcarrier level [9]. Meanwhile, in recent years, Artificial Intelligence (AI) has experienced rapid development and widespread applications in positioning fields due to their outstanding performance [10]. 3 GPP also studied AI-based positioning enhancement for indoor scenarios [11]. The AI-based solutions can potentially overcome the limitations and difficulties of traditional positioning methods, and numerical results show that deep supervised learning with CIR information can greatly improve positioning accuracy compared with traditional methods [12]. Motivated by the high performance of AI and its wide application, some research has regarded CSI as image information and finds a mapping from CSI measurements to the coordinates of the target terminals by using deep learning; these learning-based methods achieved higher positioning accuracy than traditional positioning methods [13; 14]. Meanwhile, In CSI-based or CIR-based fingerprinting approaches, AI models are able to learn the knowledge of fingerprint features offline based on the dataset of labeledfingerprints [4]. However, obtaining a large amount of labeled data is difficult and rather costly due to the need for experts' time and experience. Moreover, low-quality labeled data can adversely affect the performance of deep models. To address these issues, Deep Semi-Supervised Learning (DSSL) has been recently considered to improve learning performance by exploring potential patterns from unlabeled samples. When carefully examining the similarity of image processing and the underlying indoor location, we can find that both image processing and CIR positioning are based on feature recognition, thereby realizing the perception and understanding of user location, and obtaining position features of User Equipments (UEs) in a specific area. They also have similar feedback mechanisms in the process of model training through generating loss values when using a neural network. These observations inspire us to exploit an adapted DSSL method to handle indoor positioning tasks. Although various methods, such as the II Model, Temporal Ensembling [15], and Mean Teacher [16], have shown advantages for image classification, some modifications have to be made based on the data type and target format for positioning tasks. In this paper, we develop an Adapted Mean Teacher (AMT) model under the DSSL paradigm for indoor positioning using CIR fingerprints, which is inspired by the inherent similarity between image processing and indoor positioning, and the efficiency of the consistency regularization method. Additionally, the 5G Advanced has set high requirements for future positioning accuracy, aiming to achieve centimeter-level precision. Based on the scenario of a 5G new radio, we aim to apply the machine learning method to indoor positioning to meet the development needs of 5G Advanced. The main contributions of this paper can be summarized as follows:  
* We mathematically present the CIR estimation for building a CIR-based fingerprint dataset according to the 5G NR standard. * A tailored neural network based on Residual Network (ResNet) is designed to extract position features of CIR fingerprints to predict the position of users. In a supervised learning manner with abundant label data, it achieves sub-meter level accuracy with a mean error of 31 cm. * We propose efficient implicit random augmentation methods for CIR data by borrowing the idea of data augmentation in image processing tasks.