# Signals Intelligence System with Software-Defined Radio
## 1 Introduction
Another example of a system that combines SDR and artificial intelligence is DARPA's (Defense Advanced Research Projects Agency) Colosseum [11]. This system has immense processing power that is used in the Spectrum Collaboration Challenge. This system contains 128 USRP (Universal Software Radio Peripheral) Ettus X310 with integrated FPGAs (field-programmable gate arrays) and ATCA-3671 systems with multiple FPGAs and servers with very powerful graphics cards used for artificial intelligence. [PERSON] [12] uses artificial intelligence in RF signal processing. He analyses the performance of a \"ResNet\" type network used for signal detection that can have 24 different types of modulation. In the first phase, a database is created consisting of 24 classes of modulated signals that are acquired for SNR (signal-to-noise ratio) values between [\(-20\) dB, \(30\) dB]. Most of this data is used to train the neural network, and a smaller part is used to validate it and calculate detection accuracy. The system used consists of a computer with several dedicated video cards used for defining, training, and validating the network. In addition, two USRP B210s are used for transmitting and receiving RF signals. Mostly, intelligent neural networks are used for processing information in a 2D format (images). The RF signals have a different structure than 2D signals, and they require the definition of a different type of network than those used for image detection and processing [13] or speech signal processing [14]. In [15], an intelligent \"CNN\" (convolutional neural network) neural network is presented. It is modified to analyze and detect complex and temporal RF signals. Systems using artificial intelligence are efficient enough to process a very large amount of data. They are able to provide much faster results than any other classical algorithm. However, they also have certain limitations, being vulnerable in the event of an attack. In paper [16], the vulnerabilities of the intelligent network used in the paper [11] are analyzed. They discovered that deep learning algorithms used for signal classification are very susceptible to attacks because less signal power is needed for the attacker in order to produce misclassification, as compared to traditional jamming. The versatility shown by artificial neural networks is high, offering a great diversity of uses even within the same domain. [PERSON] presented in [17] a system that is capable of detecting and classifying: new signals received but known, new signals received but unknown, repeated attack signals received from a jamming station, and overlappingsignals. Signal reception and processing were carried out for signals at SNR levels between [0 dB, 18 dB]. This paper comes up with a new type of approach that can be used for signal modulation detection. The goal of this paper is to assess the feasibility of performing cloud-based modulation recognition using artificial neural networks and SDR. The proposed approach uses cloud computing instead of traditional hardware (CPU (central processing unit) or GPU (graphics processing unit)) in order to obtain the trained model of the artificial intelligence network. The use of cloud computing removes the limits imposed by dedicated hardware equipment by providing sufficient processing power required by the neural network. The system integration is also performed in the cloud and allows the users to connect from everywhere. The only resources they need to use the system are a computer with internet access. Section 2 presents the experimental setup together with the system architecture and definition. Section 3 illustrates the results obtained after the network training and how the resulting model is tested against real data. The discussion of the results is presented in Section 4, while Section 5 presents the conclusions drawn.