Application of artificial intelligence method in adaptive antenna system - Rozhnovskyi, M.V.; Rozhnovska, I.Yu. (Radiotekhnika, 2023)
The requirements for adaptive antenna systems in modern and future wireless networks of the fifth (5G) and sixth (6G) generations are analyzed. The block diagram of the adaptive antenna system is presented and the basic principle of its operation is described. It is proposed to improve the block diagram of a modern adaptive antenna system by integrating an artificial intelligence module into it. The principle of interaction of the artificial intelligence module with the adaptive antenna system in the block diagram is shown and described. One of the methods of artificial intelligence (machine learning), the intelligent agent, is described and its mathematical model is presented. The possibility of applying the considered method in the cellular environment of a wireless communication network to improve the operation of an adaptive antenna system is shown. An example of the operation of an artificial intelligence module as part of an adaptive antenna system using an intelligent agent method is given. It is shown that, using the machine learning method, an intelligent agent within a single wireless communication cell can create a certain knowledge system capable of understanding and learning, taking into account the patterns of subscribers’ movement within the cell and predicting the direction of movement of a particular subscriber terminal. The resulting knowledge system is formed in an artificial intelligence module, which is included in the block diagram of a modern adaptive antenna system proposed in this paper, and can potentially be used to more accurately control the directional pattern of an adaptive antenna system. The idea proposed in this paper potentially allows us to develop the concept of a smart antenna, as well as to improve the characteristics of adaptive antenna systems, namely, to increase the energy efficiency of these systems by more accurately realizing the directivity characteristics and intelligent control of the radiation pattern petals using artificial intelligence.