Abstract
In the suggested methodology, a microstrip patch antenna with a rectangular shape is employed, along with the utilization of modified ant colony optimization (MACO) algorithms. The operating frequency for the patch antenna is set at 28 GHz. The antenna is designed to exhibit a single-band response, analyzed using HFSS software and optimized results obtained using MATLAB tool. For the antenna design, the chosen substrate is FR4, with a dielectric constant of 4.4 and a thickness of 0.6 mm. The selection of FR4 substrate is based on its accessibility and cost-effectiveness. Various parameters are validated to evaluate the proposed antenna structure’s performance, including return loss, voltage standing wave ratio, radiation pattern, and gain. These measurements are further analyzed to optimize the antenna's performance using MACO approaches. The results of this suggested methodology demonstrate improved performance for the single-band microstrip rectangular patch antenna, making it a suitable choice for 5G applications.
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Ramasamy, R., Rajavel, V., Ghoshal, D., Jain, R., Nanmaran, R., Srimathi, S. (2024). An Intelligent Antenna Optimization Using Machine Learning Algorithm for 5G Applications. In: Das, S., Saha, S., Coello Coello, C.A., Bansal, J.C. (eds) Advances in Data-Driven Computing and Intelligent Systems. ADCIS 2023. Lecture Notes in Networks and Systems, vol 893. Springer, Singapore. https://doi.org/10.1007/978-981-99-9518-9_24
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