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Volume 13 | Issue 5 | Year 2026 | Article Id. IJEEE-V13I5P104 | DOI : https://doi.org/10.14445/23488379/IJEEE-V13I5P104

Machine Learning-driven Design Optimization of Antennas using Linear Regression

Archana Tiwari, S. G. Bhele, Joydeep Dutt, Shruti Dutt, Nita Nimbarte

Received Revised Accepted Published
08 Feb 2026 07 Mar 2026 06 Apr 2026 30 May 2026

Citation :

Archana Tiwari, S. G. Bhele, Joydeep Dutt, Shruti Dutt, Nita Nimbarte, "Machine Learning-driven Design Optimization of Antennas using Linear Regression," International Journal of Electrical and Electronics Engineering, vol. 13, no. 5, pp. 33-47, 2026. Crossref, https://doi.org/10.14445/23488379/IJEEE-V13I5P104

Abstract

The proposed method explores the application of Machine Learning (ML) techniques, specifically Linear Regression, to optimize antenna design for Artificial Intelligence (AI) and Internet of Things (IoT) enabled wireless communication systems. Traditional design methods relying on numerical Electromagnetic (EM) simulations are time-consuming and resource-intensive. The proposed approach leverages ML to predict antenna performance metrics, significantly reducing design time and enhancing accuracy. A Linear Regression model was trained on 300 datasets generated from HFSS simulations, achieving impressive accuracy rates of 91.94% to 97.18% for resonant frequency and 78.69% to 99.77% for return loss. The results demonstrate the potential of ML in streamlining antenna design processes, enabling the development of efficient and compact antennas for AI- and IoT-driven applications. The designed antenna was fabricated on an FR-4 substrate and characterized using a Vector Network Analyzer, validating the effectiveness of the ML-based approach.

Keywords

Antenna, Compact, Linear regression, Machine Learning, Optimization.

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