Machine Learning Based MIMO Antenna Arrays Optimization for 5G/6G

Utilization of multiple-input multiple-output (MIMO) systems as a means of increasing channel capacity has been an area of increasing consideration in radio communications. This research is important because high-frequency communication using MIMO allows for international communication at long distances using lower power consumption than many other approaches. The objective of this research is to develop and implement software algorithms for the synthesis of MIMO-type arrays, which entail an increase in the efficiency of their operation, including the suppression of side lobes by optimizing their structures, taking into account the interference of electromagnetic waves between neighboring elements, increasing the signal-to-noise ratio (SNR) at the receiver input, increasing the bandwidth of the receiving and transmitting modules of LTE/5G communication systems. Since it is assumed that 6G communication networks will use the terahertz and sub-terahertz frequency ranges and provide a significantly lower level of delay in data transmission than 5G/IMT-2020 networks, the proposed Machine Learning (ML) algorithms should be universal and capable of providing computer-aided design of aperiodic multi-element antenna arrays not only in existing LTE/5G communication systems, but also in the terahertz frequency range.