# Signals Intelligence System with Software-Defined Radio
## 2 Materials and Methods  
### Experimental Setup  
The hardware used is BladeRF 2.0 micro xA4 [18]. This is a new generation SDR equipment. It has a low price, and it is dedicated to students and people who are passionate about SDR but who have a limited budget. The equipment is versatile because it can be used by most computer operating systems (Windows, Linux, and macOS). Two of these boards are used within the paper. The characteristics of the used boards are shown in Table 1. Applications and frameworks that support this board are GNU Radio, MATLAB, Simulink, and PothosWare. This device is also used in multiple research projects such as [19; 20]. For the artificial neural network implementation, several solutions were analyzed. The analyzed solutions were selected in order to allow the implementation without being an expert in this field. The first two analyzed platforms were DLHUB [21] and ANNHUB [22]. Both of them are developed by the same company, ANSCenter (Sydney, Australia) [23]. The two platforms offer the user the opportunity to create training models in a simple and efficient way without the need for advanced knowledge in the field of artificial intelligence. The resulting model can be exported to multiple programming languages (LabVIEW, C++, Arduino, Python), where it can be integrated to form an entire system. Following the integrations and tests performed with these two platforms, the following disadvantages were found:  
* They require a computer with a very powerful processor and a dedicated video card to be able to successfully train the model;
* The configurations available are implemented to obtain models of neural networks dedicated to image and video processing; \begin{table}
\begin{tabular}{c c c} \hline \hline
**Specification** & **Value** & **Unit** \\ \hline ADC/DAC Sample Rate & 0.521–61.44 & MSPS \\ \hline ADC/DAC Resolution & 12 & bits \\ \hline RF Tuning Range (RX) & 70–6000 & MHZ \\ \hline RF Tuning Range (TX) & 47–6000 & MHZ \\ \hline RF Bandwidth Filter & \textless{}0.2–56 & MHZ \\ \hline CW Output Power & \textless{}8 & dBm \\ \hline Operating Temperature & 70 & \({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table} 
Table 1: BladeRF 2.0 micro xA4 [18]. * The training process of a model cannot run successfully if the number of input data is larger than 2048. All these disadvantages determined after testing the platforms led to their non-use in this paper. Another solution that can be used to develop and implement an artificial neural network consists of using cloud computing platforms. After analyzing the available platforms (Google Vertex AI (artificial intelligence), Microsoft Azure Auto ML (machine learning), Amazon AWS ML, IBM Watson Studio, and Oracle AI), only one was selected for further activities. The platform selected is Microsoft Azure AutoML [24]. This selection was made based on the ratios between price and performance and between configurability and performance. AutoML is a service developed by Microsoft within its research division. Its main purpose is to speed up the time needed to develop a neural network and obtain dedicated models as simply and quickly as possible. This is a big advantage over the classical approach, where powerful hardware equipment, multiple knowledge in the field of artificial intelligence, and a lot of time for analysis and development are needed. Azure AutoML enables researchers and developers to create artificial intelligence models that can be used to solve various problems, save a lot of time that would be required to develop a classic application, and streamline various processes. The three main types of processing possible within AutoML are classification, regression, and prediction [25]. The processing type used within this paper is classification. Classification is one of the most common types of AI applications. Using the data provided for training, the resulting model can predict which category new data entered into the model falls into.