# 6G Technology for Indoor Localization by Deep Learning with Attention Mechanism
## 2 Channel Modeling and Indoor Positioning System
The offline phase is essential for training the positioning model, aiming to capture the spatial patterns in the THz sensor data that correlate with specific indoor locations. This phase includes three key steps: data collection, data preprocessing, and model training. THz wireless sensors are deployed to gather raw data that reflect the indoor environment's signal characteristics for data collection. Due to the high frequency of THz signals, these data provide enhanced spatial resolution, which is critical for precise indoor positioning. The collected data underwent preprocessing steps, such as noise reduction, normalization, and feature extraction for data preprocessing. If there is a specific source of interference, we will eliminate it first by subtracting the interference from the input data. This step refines the input, ensuring that only the most relevant features are provided to the model, thus facilitating efficient learning. The preprocessed data are used to train a CNN, which learns the complex spatial features and patterns within the data for model training. CNN is trained to associate these features with physical locations in the indoor environment. This training process is computationally intensive, requiring significant data and processing power to ensure the model's accuracy and robustness. In summary, the offline phase focuses on developing a well-optimized positioning model that will be deployed for real-time applications. Figure 1: Blocks of the indoor positioning system, with an offline phase for model training on preprocessed data and an online phase for real-time location estimation. The online phase utilizes the trained model to perform real-time position estimation, streamlining the process for rapid response and continuous location updates. This phase includes two key steps: position estimation and location output. New data collected from the THz sensors is preprocessed similarly to the offline phase and then fed into the pre-trained CNN model for position estimation. The model analyzes these data to estimate the current location based on the spatial patterns it learned during the training phase. The estimated location is output in real time, enabling applications such as navigation, asset tracking, and security monitoring for location output. In essence, the offline phase builds and trains the model, while the online phase leverages this model for efficient, real-time indoor positioning. This structured approach ensures the system maintains high accuracy and low latency, even in dynamic indoor environments. The process begins with the collection of high-frequency THz signals using a wireless sensor network. These sensors capture the raw data that form the basis for further processing. The raw THz data are pre-processed to enhance the quality of the input for the machine learning model. The steps involve noise reduction, normalization, and feature extraction to ensure that the data are in the proper format for analysis. The pre-processed data are then fed into a CNN, which is responsible for learning the spatial features of the data. Next, the CNN parameters are passed to the position estimation block, which produces an estimated location based on the learned features. Finally, the estimated location is input to a module that refines the CNN output by using algorithms to achieve higher positioning accuracy.