Machine learning-inspired design of a modified spider-shaped MIMO antenna for 5G-advanced wireless communications

A modified Spider Shaped Four Element Multi Input Multi Output Antenna (SSFEMIMOA) is suggested for the 5G-advanced, and sub-6 GHz advanced wireless communications. The proposed SSFEMIMOA exhibits a low-profile and compact configuration with an overall size of 0.36λ² (52 × 52 × 1.57 mm³), achieved through the optimal placement of the constituent antenna elements. The top layer of the unit-cell antenna is designed in a modified spider-shaped geometry incorporating an additional oval-shaped structure. The modified partial ground structure alters the current distribution and electromagnetic coupling within the antenna, thereby improving impedance matching and broadening the operating bandwidth. The diversity parameters of the SSFEMIMOA have been attained by placing the elements in orthogonal position to each other. The anticipated design attains the BW ≈ 5.5 GHz (3.5-9.05) with maximum gain of 6.1 dB and isolation between -20 to -40 dB while satisfying the MIMO diversity parameters in terms of Envelope Correlation Coefficient (ECC), Directive Gain (DG), Channel Capacity Loss (CCL) and Total Active Reflection Coefficient (TARC). To enhance the design optimization process, multiple Machine Learning (ML) algorithms were employed and systematically evaluated. Based on the obtained performance metrics, Gaussian Process Regression (GPR) exhibited the best predictive capability, yielding the lowest prediction error and highest accuracy compared to the other investigated algorithms. Finally, the proposed SSFEMIMOA was fabricated and validated through measurements of S-parameters, transmission coefficients, radiation characteristics, and other relevant performance parameters. The experimental results exhibit excellent impedance matching and radiation performance, making the antenna a promising candidate for emerging 5G/6G advanced wireless communication systems.