# Signals Intelligence System with Software-Defined Radio
## 3 Results
#### System Testing  
To use the resulting artificial intelligence model, it needs to be integrated. Its integration is also carried out with the help of the \"Microsoft Azure AutoML\" service. To integrate the model, all the integration parameters need to be configured. This can take from a few minutes to a few hours, depending on the processing unit chosen. The \"Azure Container Instance\" service [28] was chosen as the processing unit because it allows quick integration, and the user does not have to manage virtual machines or other additional tools. This service allows the integration of the model without the need for additional elements.  
After completing the integration process, we moved to the testing step to validate the obtained model. For the initial system testing, data sequences from the database are used. Multiple signals for each of the 10 modulation classes are stored in this database.  
Signals for each of the 10 modulation classes are tested. The results obtained are in accordance with the obtained accuracy and the values in the confusion matrix. For signals that are collected at a lower SNR level, false detection is possible. The higher the signal strength level, the higher the possibility that the detection will be 100% correct.  
The results of running the integrated model for a \"BPSK\" modulated signal can be seen in Figure 3. On the left side of the picture can be seen the text entry where the samples of the signal to be tested are pasted. After the \"test\" button is pressed, the identified modulation type is printed on the right side of the window. The result is available in a gray box in JSON (JavaScript Object Notation) format.  
In order to test the obtained model in real conditions, an SDR test platform was used. In this, the transmission and reception of the modulated signal over a radio channel are carried out.  
To implement the SDR part of the system, the following are used:  
* Two BladeRF 2.0 micro xA4 equipment as RF hardware equipment with additional accessories (antennas, SMA (SubMiniature version A) cables, USB (Universal Serial Bus) cables).  
Figure 3: Testing for BPSK signal.  
* A laptop with the Ubuntu 18.04 operating system for running software applications and for interfacing with hardware equipment and the AI model.
* GNU Radio topologies for realizing signal processing applications.  
The first implemented topology can be seen in Figure 4. In this topology, \"BPSK\" transmission and reception are implemented. The data source is a random one that generates 8-bit integers with values between 0 and 255. The integers are divided into 1-bit symbols and \"BPSK\" modulated. The modulated symbols are then interpolated to achieve a rate of eight samples per symbol. After sampling, an RRC (root-raised-cosine) filter is applied to prepare the transmission samples. The transmission is carried out with a sampling frequency equal to 1 MHZ. The RF frequency on which the data are transmitted is equal to 2.45 GHz. The transmitted signal can be seen in Figure 5. The reception of the signal is performed on the same RF frequency as the transmission and with the same sampling frequency. The received samples are written in a \"CSV\" file. This file is then used in order to test the trained model.  
The first check is performed for the samples corresponding to the signal with \"BPSK\" modulation. Samples are copied from the file resulting from transmission and reception and entered in the input data section of the integrated model. Once the data are entered, the user needs to press the \"Test\" button. To perform the detection, the model needs a few seconds. Once the detection process is completed, the result with the detected modulation appears on the right side of the screen. The result can be seen in Figure 6.  
Figure 4: BPSK modulation/demodulation topology.  
Figure 5: BPSK Modulated Signal. (**a**) BPSK Periodogram; (**b**) BPSK Spectrum; (**c**) BPSK I/Q Samples.