# 6G Technology for Indoor Localization by Deep Learning with Attention Mechanism
## 1 Introduction  
Current 5G and 6G positioning technologies can be divided into two categories: ranging-based methods and non-ranging methods. Ranging-based methods include Time of Arrival (TOA), Time Difference of Arrival (TDOA), Angle of Arrival (AOA), Frequency Difference of Arrival (FDOA), and Received Signal Strength Indicator (RSSI) [1; 2; 3; 4]. In 2016, [PERSON] et al. proposed simultaneous localization and mapping to track moving targets and reduce the cost of WLAN positioning [1]. In 2022, Kia proposed the fusion of Ultra-Wide Band (UWB) and Wireless Fidelity (WiFi) to improve localization accuracy. The results proved that UWB and WiFi, when working together, could compensate for each other's limitations and provide a more accurate position solution [2]. Meanwhile, [PERSON] proposed a positioning model for Non-Line-of-Sight (NLOS) millimeter-wave, providing an analytical solution for the Cramer-Rao lower bounds [3]. In 2024, [PERSON] proposed the time difference alignment errors (TDAEs) based on the TDOA and time double difference-of-arrival model. Results of both simulation and real-world experiments show that the positioning accuracy using the proposed method could reach the submeter level [4]. Due to the properties of 6G signals, such as short wavelength, narrow beam, and good directionality, indoor positioning methods for 6G can achieve higher accuracy in distance and angle measurement. By combining Multi-input Multi-output (MIMO) antennas and beamforming technology, stronger beams can be obtained, leading to better angular resolution and improved positioning accuracy of AOA. As the carrier frequency of 6G communication systems increases, higher signal bandwidth in the frequency domainmakes it easier to improve the accuracy of positioning technologies such as TOA, TDOA, FDOA, and RSSI [5; 6; 7; 8; 9; 10]. Non-ranging positioning methods for channel state information (CSI) are also popular [11; 12; 13; 14; 15; 16]. CSI provides more detailed channel information as it reflects the characteristics of channels. Recent CSI-related literature can be categorized into algorithmic methods and deep learning methods. Algorithmic research on fingerprinting was developed in 2013 by [PERSON] et al. They proposed the use of channel information combined with correlation calculations to determine excitation functions for positioning [11]. In 2018, [PERSON] et al. used CSI to create fingerprints and applied multidimensional scaling algorithms and the K-nearest neighbors algorithm to estimate locations [12]. In 2019, [PERSON] et al. proposed the use of beamforming to reduce positioning errors in millimeter-wave MIMO systems [13]. In 2020, [PERSON] et al. derived a mathematical analytical solution to find the theoretical upper limit of positioning errors [14]. In 2021, [PERSON] et al. proposed a multipath-assisted indoor positioning system using Wi-Fi signals [15]. In 2023, [PERSON] et al. proposed a trajectory-robust localization algorithm for localization. Experimental results demonstrated that this method was able to improve localization accuracy and save computation time [16]. Compared to the geometric methods mentioned above, fingerprinting methods have the main advantage of being robust to multipath signals. This is because fingerprinting methods transform the positioning problem into a pattern-matching problem by dividing the localization process into offline training and online positioning stages. In fingerprinting methods, the characteristics of a location depend on the detected signal pattern. Therefore, fingerprinting does not require distance or angle measurements, making it especially feasible in cluttered environments such as indoor or urban scenarios. Localization with deep learning techniques can improve positioning accuracy and application to IoT [17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27]. In 2020, [PERSON] et al. proposed long short-term memory neural networks for positioning. Experimental results showed that this method could effectively enhance the accuracy of indoor positioning [17].