The performance of Wi-Fi fingerprinting indoor localization systems (ILS) in indoor environments depends on the channel state information (CSI) that is usually restricted because of the fading effect of the multipath. Commonly referred to as the next positioning generation (NPG), the Wi-Fi, IEEE 802.11az standard offers physical layer characteristics that allow positioning and enhanced ranging using conventional methods. Therefore, it is essential to create an indoor environment dataset of fingerprints of CIR based on 802.11az signals, and label all these fingerprints by their location data estimate STA locations based on a portion of the dataset for fingerprints. This work develops a model for training a convolutional neural network (CNN) for positioning and localization through generating IEEE 802.11data. The study includes the use of a trained CNN to predict the position or location of several stations according to fingerprint data. This includes evaluating the performance of the CNN for multiple channel impulses responses (CIRs). Deep learning and Fingerprinting algorithms are employed in Wi-Fi positioning models to create a dataset through sampling the fingerprints channel at recognized positions in an environment. The model predicts the locations of a user according to a signal acknowledged of an unidentified position via a reference database. The work also discusses the influence of antenna array size and channel bandwidth on performance. It is shown that the increased training epochs and number of STAs improve the network performance. The results have been proven by a confusion matrix that summarizes and visualizes the undertaking classification technique. We use a limited dataset for simplicity and last in a short simulation time but a higher performance is achieved by training a larger data.

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