## 1 Introduction  
With the rapid development of Internet of Things technology, location-based services such as navigation and positioning are receiving extensive attention. The global navigation satellite system (GNSS) works well in an outdoor environment [1]. However, in a complex indoor environment, the signal of satellite is weakened due to blockage and multipath propagation. GNSS often has poor accuracy or even fails to work. In order to obtain satisfactory indoor location, many methods have been proposed, such as Ultra-Wide Band (UWB), Wi-Fi technology, Bluetooth and so on [2; 3; 4]. However, they are not widely deployed and not affordable for personnel positioning during epidemic control for COVID-19 or large-scale exhibition, such as Import Expo. The existing cellular network is suitable for these location-based services adjusting to the new times, and there is no need to lay a large number of special hardware. From the first generation (1G) to the fifth generation (5G) mobile communication system, each generation has relevant research contributions for positioning [5]. 2G and 3G have supported standard positioning methods including timing advance (TA), enhanced observed time difference (EOTD), uplink time difference of arrival (UTDOA) and GNSS-based method [6]. In the 4G standard for both the control plane and the user plane, many positioning methods are available, such as Cell ID (CID), user equipment (UE)-assisted and network-based enhanced CID (E-CID), UE-based and UE-assisted A-GNSS, UE-assisted observed time difference of arrival (OTDOA) and UTDOA [7]. With the evolution of communication technology and the deployment of 5G base stations (BS), the radio signal is easy to collect. People can easily connect mobile devices such as smart phones to 5G systems in indoor environments. At the same time, 5G has the advantages of larger bandwidth and flexible subcarrier spacing, which leads to higher positioning accuracy. Many researchers have proposed a wide range of machine learning (ML)-based indoor localization approaches using fingerprints [8]. Random forest (RF)-based location awareness is used to improve the execution time and accuracy [9]. They utilize both the received signal strength indication (RSSI) and basic service set identifier (BSSID) measurements. Ref. [10] combines grid search-based kernel support vector machine (SVM) and principle component analysis (PCA) to improve the localization accuracy. Ref. [11] calculates the position of UE in 5G system by combining Kalman filter (KF), Universal Kriging (UK) spatial interpolation algorithm and k-Nearest Neighbors (KNN). However, traditional ML algorithms usually learn the shallow features of the data, which makes them unable to extract all reliable features from the complex received signal strength (RSS) fingerprints. In recent years, the indoor fingerprinting positioning system based on deep learning shows better performance than the traditional methods. Deep-Fi [12] uses a deep neural network (DNN) with four hidden layers in the Wi-Fi system to train the channel state information (CSI) of all subcarriers or antennas. DNN is very sensitive to the changes of input data, as when the data set is not sufficient, the accuracy is not high. [PERSON] [13] proposed the first Wi-Fi location algorithm based on convolutional neural network (CNN). The CNN is composed of three convolution layers and two fully connected layers, which transforms the location problem into an image classification problem [14]. They group 30 CSI measurements for 30 subcarriers for one antenna at the same reference point to construct a \(30\times 30\) matrix. Due to the limitation of hardware, not all base stations support CSI reporting. Ref. [15] uses CNN and Bluetooth RSS to classify the floor and location. The RSS of 144 Bluetooth APs collected at each reference point are converted into \(12\times 12\) eigenvector image. However, the fingerprinting database based on RSS images needs lots of base stations to achieve satisfactory positioning accuracy.