Microstrip Antenna Design for 2.4 GHz RF Energy Harvesting Circuits with Artificial Neural Networks

Yıl 2024, Cilt: 4 Sayı: 1, 33 - 38, 28.06.2024

Burak Dökmetaş Mehmet Ali Belen

Öz

This study explores the synthesis of microstrip antennas designed for 2.4 GHz RF energy harvesting circuits through the integration of artificial neural networks (ANNs). Utilizing a 3D electromagnetic (EM) simulation tool, extensive datasets were generated for training and testing the ANN model. A meticulous trial-and-error process was employed to optimize critical hyperparameters, including the number of hidden layers, neurons per layer, and activation function types. The outcome of this process was the identification of an optimal ANN model, proficient in accurately capturing complex relationships between antenna design parameters and energy harvesting efficiency. The integration of the 3D EM simulation tool and the tuned ANN model facilitated a computationally efficient approach to antenna optimization, reducing reliance on resource-intensive simulations. This research contributes to the advancement of RF energy harvesting systems, showcasing the potential of artificial intelligence in streamlining the design process for optimal microstrip antennas in 2.4 GHz applications. The demonstrated methodology provides insights into the future of computational design, offering a swift and efficient path for meeting the evolving demands of wireless communication and sensor technologies.

Anahtar Kelimeler

ANN, Microstrip Antenna, Artificial Neural Networks

Kaynakça

• B. Dokmetas, and G.O. Arican, “Design of dual-band SIW antenna for millimeter-wave communication,” 31st Telecommunications Forum (TELFOR), pp. 1-4, November, 2023.
• W. A. Khan, R. Raad, F. Tubbal, P. I. Theoharis, and S. Iranmanesh. “RF energy harvesting using rectennas: A comprehensive survey,” IEEE Sensors Journal (2024).
• J. Szut, P. Piątek, and M. Pauluk, “RF energy harvesting Energies,” vol. 17, no. 5, 2024.
• V. Kuhn, C. Lahuec, F. Seguin and C. Person, “A multi-band stacked RF energy harvester with RF-to-DC efficiency up to 84%,” IEEE Transactions on Microwave Theory and Techniques, vol. 63, no. 5, pp. 1768-1778, May 2015.
• X. Zhang, J. Grajal, M. López-Vallejo, E. McVay, and T. Palacios, “Opportunities and challenges of ambient radio-frequency energy harvesting,” Joule, vol. 4, no. 6, pp. 1148-1152, 2020.
• P. Mahouti, “Design optimization of a pattern reconfigurable microstrip antenna using differential evolution and 3D EM simulation‐based neural network model,” International Journal of RF and Microwave Computer‐Aided Engineering, vol. 29, no. 8, 2019.
• S. Roshani, S. Koziel, S. I. Yahya, M. A. Chaudhary, Y. Y. Ghadi, S. Roshani, and L. Golunski, “Mutual coupling reduction in antenna arrays using artificial intelligence approach and inverse neural network surrogates,” Sensors, vol. 23, no. 16, 2023.
• P. Pragya, and J. S. Sivia, “Design of minkowski curve-based slotted microstrip patch antenna using artificial neural network,” Journal of The Institution of Engineers (India): Series B, vol. 104, no. 1, pp. 129-139, 2023.
• M. Mahouti, N. Kuskonmaz, P. Mahouti, M. A. Belen, and M. Palandoken, “Artificial neural network application for novel 3D printed nonuniform ceramic reflectarray antenna,” International journal of numerical modelling: electronic networks, devices and fields, vol. 33, no. 6, 2020.
• D. Prabhakar, P. Karunakar, S. V. Rama Rao, and K. Srinivas, “Prediction of microstrip antenna dimension using optimized auto-metric graph neural network,” Intelligent Systems with Applications, vol.21, 2024.
• R. Ramasamy and M. A. Bennet, “An efficient antenna parameters estimation using machine learning algorithms,” Progress In Electromagnetics Research C, vol. 130, pp. 169-181, 2023.
• S. Koziel, N. Çalık, P. Mahouti and M. A. Belen, “Accurate Modeling of Antenna Structures by Means of Domain Confinement and Pyramidal Deep Neural Networks,” IEEE Transactions on Antennas and Propagation, vol. 70, no. 3, pp. 2174-2188, March 2022.
• A. Papathanasopoulos, P. A. Apostolopoulos and Y. Rahmat-Samii, “Optimization assisted by neural network-based machine learning in electromagnetic applications," IEEE Transactions on Antennas and Propagation, vol.72, no.1, pp.160-173, 2023.
• S. Koziel, “Fast simulation‐driven antenna design using response‐feature surrogates,” International Journal of RF and Microwave Computer‐Aided Engineering, vol. 25, no. 5, pp. 394-402, 2015.
• S. Koziel, N. Çalık, P. Mahouti and M. A. Belen, “Reliable computationally efficient behavioral modeling of microwave passives using deep learning surrogates in confined domains,” IEEE Transactions on Microwave Theory and Techniques, vol. 71, no. 3, pp. 956-968, March 2023, doi: 10.1109/TMTT.2022.3218024.
• B. Si, Z. Ni, J. Xu, Y. Li, and F. Liu, “Interactive effects of hyperparameter optimization techniques and data characteristics on the performance of machine learning algorithms for building energy metamodeling,” Case Studies in Thermal Engineering, 2024.
• X. Ying, “An overview of overfitting and its solutions,” In Journal of physics: Conference series, vol. 1168, IOP Publishing, 2019.
• S. Koziel, N. Çalık, P. Mahouti, and M. A. Belen, “Accurate modeling of antenna structures by means of domain confinement and pyramidal deep neural networks,” IEEE Transactions on Antennas and Propagation, vol. 70, no. 3, pp. 2174-2188, 2021.
• N. Calik, F. Güneş, S. Koziel, A. Pietrenko-Dabrowska, M. A. Belen, and P. Mahouti, “Deep-learning-based precise characterization of microwave transistors using fully-automated regression surrogates,” Scientific Reports, vol. 13, no. 1, 2023.
• N. G. Reich, J. Lessler, K. Sakrejda, S. A. Lauer, S. Iamsirithaworn, and D. AT Cummings. “Case study in evaluating time series prediction models using the relative mean absolute error,” The American Statistician, vol. 70, no. 3, 2016.
• P. Mahouti, A. Belen, O. Tari, M. A. Belen, S. Karahan, and S. Koziel, “Data-driven surrogate-assisted optimization of metamaterial-based filtenna using deep learning,” Electronics, vol. 12, no. 7, 2023.