Authors : Sri Chandrika Naidu; Balaji Pachharu; Mahanwith Karanam; Saidu Babu Kumbha

Volume/Issue : Volume 11 - 2026, Issue 4 - April

Google Scholar : https://tinyurl.com/3eaewpnu

Scribd : https://tinyurl.com/mvy42y2k

DOI : https://doi.org/10.38124/ijisrt/26apr1449

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : This paper presents the design, simulation, and machine learning-assisted optimization of a compact multiband MIMO antenna incorporating fractal geometry. Multi-band MIMO antennas play a vital role in modern wireless communication systems by enabling operation across multiple frequency bands while maintaining high data rates and reliable performance. However, designing compact multi-band antennas with stable impedance matching, high isolation, and consistent radiation characteristics remains a challenging task. To address these challenges, a fractal-based antenna structure is employed, where the self-similar geometry enhances multi-band behavior and improves space utilization. Machine learning models are utilized to predict the reflection coefficient (|S11|) based on antenna design parameters, thereby reducing dependency on repeated full-wave simulations. Further optimization is carried out using Particle Swarm Optimization (PSO) and Moth-Flame Optimization (MFO) algorithms to enhance bandwidth and overall antenna performance. The proposed approach improves computational efficiency, supports antenna miniaturization, and achieves accurate prediction of optimal design parameters. The optimized multi-band MIMO antenna demonstrates improved reflection characteristics and stable performance, making it suitable for high-performance wireless communication applications.

Keywords : Multi-Band MIMO Antenna, Fractal Geometry, Machine Learning, Antenna Optimization, Gaussian Process Regression, Random Forest Regression, Particle Swarm Optimization, Moth-Flame Optimization, Reflection Coefficient (|S11|).

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