###### Abstract  
The performance of the services provided by Global Navigation Satellite Systems (GNSSs) can be seriously degraded by the presence of wireless interferences, and Machine Learning (ML) has been applied to address this problem using the digital artifacts generated by the GNSS receiver. While such an application is not novel in the literature, the analysis of the impact of the bit-depth at which the GNSS signal is recorded has not received significant attention. The type and power level of the wireless interference are also important factors to investigate in this context. This paper addresses this gap by performing an extensive analysis of the impact of these factors on a data set of GNSS signals subject to three different types of wireless interferences with ML and DL algorithms. The analysis is a combination of a pre-processing phase where the Carrier-to-Noise Ratio (CNR) values of different satellites are evaluated, the extraction of relevant features for ML, and the application of a Convolutional Neural Network (CNN) with a multi-head attention layer. The results show that the proposed approach is able to detect the presence of interference with great accuracy (e.g., 99%) but the type of interference and bit-depth can decrease the performance.  
GNSS; machine learning; deep learning; wireless interference 202514114/11/17/16/12025/14/11/17/17  
The underlying concept of this paper is to apply ML and DL algorithms to the detection and classification of interferences of GNSSs taking into consideration the specific aspects of the GNSS context: (1) the digital observables identified with the Carrier-to-Noise Ratio (CNR) from the GNSS receivers are the result of a pre-processing step where discriminating information of the wireless interferences may be lost, (2) GNSS satellites may not be fully visible at the time of the data acquisition and processing, which can also obfuscate important information on the interferences, and (3) the signal may be processed using different bit-depths as there is a trade-off between processing time and storage.  
The aim of this paper is to investigate the application both of'shallow' (e.g., Decision Tree) and neural networks 'deep' ML algorithms to detect and classify different types of wireless interferences in GNSS data collected from real measurements and processed at different power levels and bit-depths. Additional details on the novelty and advancement of this study in comparison to the state of the art in the research literature are provided at the end of Section 2.  
The structure of this paper is the following: Section 2 provides a literature review on the application of ML and DL to the problem of detection and classification of wireless interference in GNSS signals. Section 3 outlines the overall methodology used for the implementation of data collection, pre-processing, and the application of the ML and DL algorithms. In particular, Section 3.2 describes the ML and DL algorithms used to perform the detection and classification including the Convolutional Neural Network (CNN) architecture and the features used in the ML application. Section 4 describes the data set used to evaluate the approach. The data set was generated by the authors in their laboratory and it will be available to the research community after the publication of the paper. Section 5 provides the results of the comprehensive analysis of the application of the proposed approach on the data set for different values of the hyper-parameters (e.g., number of considered satellites) and the types of wireless interference. Finally, Section 6 concludes the paper and outlines potential future developments.