# Generalization Investigation for Artificial Intelligence-Based Positioning in IIoT
## 1 Introduction  
In recent years, positioning plays a more and more important role in both the consumer market and the vertical industries. In the consumer market, the positioning enables the services such as shopping guides in shopping malls, and self-service tour guides. In vertical industries, positioning could facilitate intelligent manufacturing and emergency rescues [1]. In all the positioning services especially in the vertical industries, high positioning precision is one crucial requirement. In the 5G defined in the 3 rd Generation Partnership Project (3 GPP), positioning technology is always an important technique field. In the 5G system, 3 GPP specified multiple positioning solutions such as timing difference of arrival (TDOA), and multi-cell round trip time (Multi-RTT) [2] to enable the positioning service. The target scenarios in 5G release 16 are mainly the commercial scenarios and the target accuracy requirements are defined as follows [3]:  
* Horizontal positioning error < 3 m for 80% of devices in indoor deployment scenarios;
* Vertical positioning error < 3 m for 80% of devices in indoor deployment scenarios;
* Horizontal positioning error < 10 m for 80% of devices in outdoor deployments scenarios;
* Vertical positioning error < 3 m for 80% of devices in outdoor deployment scenarios. In 5G release 17, enhancement of the positioning solutions was carried out. The main target scenarios are the IIoT scenarios and the defined target accuracy requirements are as follows [4]:  
* Horizontal position accuracy (<0.2 m) for 90% of devices
* Vertical position accuracy (<1 m) for 90% of devices  
However, according to the evaluation and study in release 17, it is observed that it is quite challenging to achieve the high precision required in IIoT scenarios by using traditional positioning algorithms. The main reason is that the achieved accuracy of traditional positioning algorithms highly depends on the number of line of sight (LOS) paths. While, in the IIoT scenario, due to the distribution of different blocks, the probability of the LOS path is quite low. In addition, timing-based positioning solutions such as TDOA or multi-RTT are quite sensitive to timing synchronization. However, the network synchronization error and the receiving timing error on the device side would further degrade the positioning accuracy. These years, artificial intelligence (AI) technology has developed rapidly. It shows the excellent capability to handle complicated processing. Researchers consider utilizing this tool to improve positioning accuracy [5; 6]. The efficient feature extraction function of AI can be used to extract the abstract features of the measurement information of the channel from the base station to the user, which may bring a major breakthrough to solve the above problems. For AI-based solutions, generalization capability is one important metric. It demonstrates the ability to adapt to different environments. Good generalization capability guarantees performance in various environments. In the IIoT scenarios, the clutter distribution, network synchronization, and receiving timing error on the device side may vary in time or with the change in indoor layout. However, currently, there is no literature to investigate the generalization capability against the change in clutter distribution, network synchronization, and receiving timing errors. In this paper, we will fill this blank area. This article will first evaluate the generalization performance of AI-based positioning when changing the clutter distribution, network synchronization error and device receiving timing error, respectively. Then, existing popular solutions to improve the generalization will be studied and compared to figure out the feasibility in IIoT scenarios. At last insightful observations would be drawn, which would be good guidance for the model development in realistic scenarios. The structure of the paper is as follows. Related works are summarized in Section 2. The system model is introduced in Section 3.