# Signals Intelligence System with Software-Defined Radio
## 1 Introduction  
With the accelerated development of wired communications, but especially of wireless mobile communications, both terrestrial and satellite, the available frequency spectrum is increasingly reduced and disrupted. The transmission and reception of modulated signals must be performed quickly to keep up with all the demands and efficiently to make full use of the frequency spectrum that is available. An effective way to develop a mobile communication system from the point of view of development time and system testing, as well as from a financial point of view, is to use \"Software Defined Radio\" (SDR) [1; 2; 3]. This concept allows the use of powerful dedicated signal processing hardware for system simulation, implementation, and testing. SDR reconfiguration allows a wide range of applications to be developed on the same hardware, all controlled by software. SDR equipment is very versatile and efficient, but it also requires competent software. The use of artificial intelligence allows the creation of complex and efficient applications that can process a very large number of signals without the need to implement various classical algorithms [4; 5; 6], which are difficult to understand, translate into software code, and will run much slower than an artificial neural network. The artificial neural networks, once trained with a sufficiently large and complete data set, will be able to recognize and process the received signals in a proper way. This paper presents the definition and implementation of a SIGNINT System (\"Signals intelligence\") based on the advanced software-defined radio concept (with programmable digital modulation schemes and software-defined signal processing blocks) and artificial intelligence. These two elements presented above, SDR and artificial neural networks, are used in this paper to identify and classify modulated signals that are received and found in the database used for training. This helps a lot in systems working with multiple data modulations, eliminating the need to implement a complex and inefficient system for identifying the received signal. In addition to the economic efficiency of the envisioned solution and the high level of novelty it comes up with, this paper also includes the combination of broadband \"front-end\" equipment and powerful neural networks. They create a system that integrates artificial intelligence and deep learning techniques with the SDR platform. In order to identify what is the current status in the field of SDR, artificial intelligence, and SIGINT, several papers were analyzed. The analyzed papers were selected to be as close as possible to the system envisioned within this paper. From these papers, we extracted what are the strong points and what can be performed to improve the available solutions. In [7, 8], the reasons why SDR is the concept that can be perfectly used in the development of SIGINT systems are described. It helps the manufacturers to overcome the biggest problems encountered when using standard processing equipment. The degree of processing required for military radio equipment is much higher and more specialized than in previous years. The continued presence of a hostile environment is an optimization problem that is slowly becoming more and more difficult to master. Optimizing to a level close to 100% in the case of very complex RF (radio frequency) systems is almost impossible to achieve. Over time the designers of such systems have relied on simplified models that do not fully correspond to reality and fail to accurately capture real signals. Even though the individual components were increasingly optimized, the final system was limited. All these problems can be removed by using new SDR platforms combined with deep neural networks [9]. In paper [10], the SDR concept and convolutional neural networks are used together to develop a system that uses noise to modulate standard I/Q (in-phase/quadrature) samples to increase data transmission security.