## 8 Conclusions  
In this paper, we categorized wireless localization technologies into three distinct groups: range-based, range-free, and hybrid methods. Range-based technologies measure physical quantities of radio waves to estimate real-time position. In contrast, range-free technologies estimate position by utilizing existing information rather than analyzing the received radio waves, and hybrid technologies complement each other's drawbacks to reduce localization errors.  
Next, we provided three main external hindering factors that degrade the performance of these localization technologies. Firstly, NLoS occurs when communication between the anchor and device takes place through a different path due to reflection and diffraction, rather than a direct LoS path. Secondly, spatiotemporal asynchronization refers to performance degradation caused by changes in the coordination of space-time between the anchor and device due to differences in the internal system clocks of the anchor and device, or due to the relative rotations of the device. Finally, signal quality deterioration refers to degradation caused by various factors including additive noise, and so on.  
To address these hindering factors, we classified machine learning algorithms that can be combined with wireless localization technologies into two distinct categories: machine learning algorithms without ANN (such as \(K\)-NN, SVM, and RF) and with ANN (such as CNN, RNN, LSTM, AE, GNN, GAN, and VAE). Lastly, we presented examples of cases where the three main external hindering factors mentioned earlier are addressed using these algorithms. Although AI-assisted wireless localization technologies have overcome various limitations, there are still challenges to be addressed, such as issues of user privacy, data heterogeneity and data insufficiency. It is expected that these issues will be appropriately addressed in future research related to more advanced techniques, such as federated learning, distillation-based models, and algorithm using the low-rank property of the matrix. Additionally, 6G or further spotlight technologies, such as ISAC and RIS, are anticipated to demonstrate outstanding positioning performance, especially when combined with AI.  
Conceptualization, K.-J.C. and J.-B.L.; methodology, [PERSON]-J.C. and J.-B.L.; validation, M.O. and W.-H.L.; investigation, [PERSON]-J.C. and J.-B.L.; writing--original draft preparation, K.-J.C. and J.-B.L.; writing--review and editing, M.O. and W.-H.L.; visualization, K.-J.C. and J.-B.L.; supervision, W.-H.L.; project administration, W.-H.L.; funding acquisition, W.-H.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Regional Innovation Strategy (RIS) through the NRF funded by the Ministry of Education (MOE) (2021 RIS-004). Grant funded by the Korean government (MSIT) (No. 2022 RIF1A1068980).  
Not applicable.  
This work was supported by the National Research Foundation of Korea (NRF).  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AI & Artificial Intelligence \\ ANNs & Artificial Neural Networks \\ GNSS & Global Navigation Satellite System \\ GPS & Global Positioning Systems \\ IPS & Indoor Positioning System \\ LBS & Location-Based System \\ APs & Access Points \\ WLANs & Wireless Local Area Networks \\ RSSI & Received Signal Strength Indicator \\ RTT & Round Trip Time \\ UWB & Ultra-Wide Band \\ AoA & Angle of Arrival \\ DR & Dead Reckoning \\ D2D & Device-to-Device \\ OWR & One-Way Ranging \\ PDR & Pedestrian Dead Reckoning \\ RSS & Received Signal Strength \\ SNR & Signal-to-Noise Ratio \\ TDOA & Time Difference of Arrival \\ ToA & Time of Arrival \\ TWR & Two-Way Ranging \\ LoS & Line of Sight \\ NLoS & Non-Line of Sight \\ \end{tabular}