Adjusting the parameters of mobile MIMO systems using artificial intelligence
Abstract
The methods of simulation design of MIMO systems using artificial intelligence are considered. Genetic algorithms can be used to optimize the configuration of antennas and parameters of the MIMO system. AI can simulate various combinations of parameters, evaluate their performance, and evolutionarily determine the optimal settings. Neural networks can be used to predict the communication channel and optimize transmission strategies. They can learn to model the complex relationships between channel properties and MIMO system performance. Reinforcement learning techniques can be used to solve the problem of controlling signal transmission in a MIMO system. AI can interact with a dynamic environment, learn optimal signal transmission strategies, and adjust them in real time. AI can be used to develop decision support algorithms in MIMO systems. This may include making decisions on the optimal transmission mode, changing antenna or channel settings in response to changing communication conditions. AI can also use automatic learning to adapt the MIMO system to changing communication conditions.
The results of research on mobile MIMO systems using artificial intelligence form the prerequisites for expanding the capabilities and improving the performance of such systems by integrating AI technologies. The use of AI allows solving the tasks of optimizing and automatically adjusting the parameters of MIMO systems, as AI can effectively analyze large amounts of data, model various scenarios, and set optimal settings, which leads to improved MIMO system performance. AI can be used to predict channel properties in MIMO systems and manage the channel in real time. It can analyze the state of the channel, predict its changes, and adaptively respond to them, which helps to improve the quality of communication. AI can also be used to solve the problem of interference that occurs in MIMO systems by being able to analyze and manage the power distribution between antennas, determine optimal signal transmission strategies, and ensure that the impact of interference on communication quality is minimized. As a result, AI allows MIMO systems to adapt to changing communication conditions, such as changes in noise, interference, and user mobility. Thus, modeling of mobile MIMO systems using artificial intelligence is of practical importance, as it allows to improve performance, reduce costs, increase energy efficiency, and improve the quality of user experience in mobile networks
References
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