Artificial intelligence-driven automated mutual coupling reduction topology for frequency reconfigurable antenna arrays

Reconfigurable antennas alter polarization, operating frequency, or far-field pattern to accommodate changing system characteristics. For a frequency, a Reconfigurable Antenna Array (RAA), an autonomous Artificial Intelligence driven design approach for reduction of mutual coupling, and a new isolator are presented. In addition, the optimization technique for a realistic RAA commonly has several switching and a high real-time requirement. An Adaptive Unit-based Genetic Algorithm (AUGA) is utilized in this research to address the performance issue of the RAA. The reconfigurable array element may operate in the 2.5 GHz ISM band or the 3.4 GHz WiMAX channel. The suggested isolator reduces the mutual coupling in upper and lower bands by 8 and 7 dB correspondingly. The reconfigurable array was developed and tested, and measurements accord well with models, proving the concept's viability. Although this communication uses a specific antenna, the proposed Artificial Intelligence-driven design technique is general and may simply be applied to various array topologies.