Analysis of the problems of predicting the behavior of cognitive radio systems

The intellectualization of information technology systems is an urgent direction in the development of society, and one example is cognitive radio systems with the ability to self-study. The paper examines the features of cognitive radio systems that cause a change in approaches to their modeling and test planning, which is of critical importance for radio systems designed to function in conditions of electronic conflict. The purpose of the work is to analyze the problems of modeling and forecasting the knowledge-based behavior of cognitive radio systems in order to identify fundamental differences in the content of their testing system. A hypothesis is formulated about the influence of the ability of unlimited self-learning of cattle on the potential possibility of modeling its behavior in a real non-deterministic environment. The mass forecasting problem inherent in information technology systems with unlimited self-learning abilities has been formalized. To test the hypothesis, a theorem on the algorithmic insolubility of the mass problem of predicting the behavior of cattle based on an unlimited set of generated knowledge, with the non-deterministic nature of the real environment, is formulated and proved. The necessity of developing requirements for the data on which the cattle will be tested is shown. A number of tasks have been proposed that must be solved to ensure effective cattle testing. The theoretical significance of the work lies in the fact that a massive problem has been formulated that is of interest to researchers involved in the creation of self-learning information technology systems. A number of tasks proposed as a result of the analysis that should be solved to ensure the testing of cognitive radio systems are of practical importance.