Mutual Coupling Reduction in Antenna Arrays Using Artificial Intelligence Approach and Inverse Neural Network Surrogates

This paper presents a novel approach to reducing undesirable coupling in antenna arrays using custom-designed resonators and inverse surrogate modeling. To illustrate the concept, two standard patch antenna cells with 0.07? edge-to-edge distance were designed and fabricated to operate at 2.45 GHz. A stepped-impedance resonator was applied between the antennas to suppress their mutual coupling. For the first time, the optimum values of the resonator geometry parameters were obtained using the proposed inverse artificial neural network (ANN) model, constructed from the sampled EM-simulation data of the system, and trained using the particle swarm optimization (PSO) algorithm. The inverse ANN surrogate directly yields the optimum resonator dimensions based on the target values of its S-parameters being the input parameters of the model. The involvement of surrogate modeling also contributes to the acceleration of the design process, as the array does not need to undergo direct EM-driven optimization. The obtained results indicate a remarkable cancellation of the surface currents between two antennas at their operating frequency, which translates into isolation as high as -46.2 dB at 2.45 GHz, corresponding to over 37 dB improvement as compared to the conventional setup.

Automated summary

This paper presents a new approach to reduce undesirable coupling in antenna arrays using custom-designed resonators and inverse surrogate modeling. The concept is illustrated with the design and fabrication of two standard patch antenna cells operating at 2.45 GHz. A stepped-impedance resonator is applied between the antennas to suppress mutual coupling. The proposed inverse artificial neural network (ANN) model, constructed from EM-simulation data and trained using the particle swarm optimization (PSO) algorithm, obtains the optimum values of the resonator geometry parameters.