Design Optimization of Wearable Multiband Antenna Using Evolutionary Algorithm Tuned with Dipole Benchmark Problem

This paper presents the design of a novel fabric-based multi-band microstrip antenna in mm-wave frequencies for wearable applications. The reference patch antenna was etched on a flexible polytetrafluoroethylene (PTFE) fabric substrate with an overall dimension of 18 mm × 18 mm × 0.6 mm and optimized the patch geometry using a binary-coded genetic algorithm. The algorithm iteratively creates a new shape of the path surface, evaluates the cost function, and returns the best-fitted geometry based on the formulated fitness function. The free space and on-body simulation of the best-fitted antenna performance parameter was investigated and analyzed. In free space, the proposed antenna is resonant at five distinct frequencies: 27.8 GHz, 30.3 GHz, 40.1 GHz, 47.2 GHz, and 56.7 GHz. The antenna achieves a wide bandwidth of 0.69, 2.32, 2.22, 1.76, and 8.11 GHz and an improved broadside directivity of 10.3, 8.5, 7.8, 9.6, and 8.9 dB in free space, respectively. For on-body analysis, the antenna was simulated using a three-layer human body phantom model at three distinct distances. The gain and radiation efficiency were significantly reduced when the antenna was close to the phantom model and gradually enhanced as the gap increased. Moreover, the antenna performances were evaluated and compared by using four additional fabric substrates. Because of its excellent on-body performance with flexible textile-based substrates, the optimized antenna is a suitable candidate for multi-band body-centric communications.

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Knowledge‐Based Methodology of CPW‐Fed Open Stub Loaded C‐Shaped Microstrip Antenna by Surrogate‐Based Modeling

Gocen,

Akdag,

Mahouti

et al. 2024

International Journal of RF and Microwave Computer-Aided Engine

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Antenna systems with more complicated geometries have been created as a result of evolving technology and rising performance standards in the industry. The geometric complexity of modern antennas makes it difficult for circuit theory tools or parametric studies to produce adequate findings, making it difficult for designers to create the designs they want. Even though full‐wave electromagnetic (EM) modeling tools are widely utilized today, they are computationally expensive for local optimization alone. In order to speed up the stages of the simulation‐based design of high‐performance systems, many strategies have been devised to solve and/or minimize this challenge. Thanks to their adaptability, affordable computing costs, and widespread usage, surrogate‐based models have grown to be a well‐known branch. Herein, an innovative knowledge‐based methodology for building a coplanar waveguide (CPW)‐fed antenna using surrogate models is presented. In this work, a knowledge‐based methodology using surrogate modeling is applied as an advanced approach that combines domain‐specific knowledge with surrogate models to optimize the performance of a microwave antenna in a computationally efficient manner. For this aim, firstly, a 3D‐EM simulator is deployed to generate a dataset for a deep learning‐based surrogate model. When compared to the conventional optimization approach using direct deployment of EM simulators which is around 47.2 h, the proposed surrogate model approach has an average cost reduction of over 50% which corresponds to a total computational time of 24.3 h. The collected results are compared to performance metrics for prototype antenna designs as well as simulated outcomes from EM simulators and counterpart works from the literature.

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Design Optimization of Wearable Multiband Antenna Using Evolutionary Algorithm Tuned with Dipole Benchmark Problem

Cited by 3 publications

References 17 publications

Machine Learning Based Antenna Design

Machine Learning Based Antenna Design

Multi-Band mm-Wave Wearable Antenna Synthesized with a Genetic Algorithm

Knowledge‐Based Methodology of CPW‐Fed Open Stub Loaded C‐Shaped Microstrip Antenna by Surrogate‐Based Modeling

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