New modeling of reconfigurable microstrip antenna using hybrid structure of simulation driven and knowledge based artificial neural networks

Abstract

Knowledge-based modeling has a critical role to embed existing knowledge to improve modeling performance. Since reconfigurable antenna can provide more operational frequencies than the classical antennas, a knowledge-based hybrid structure is used in this work to obtain efficient model and producing optimum new models for a reconfigurable microstrip antenna. The hybrid structure consists of two phases. The first phase generates initial knowledge which is used in knowledge-based modeling structure to obtain design parameters. Artificial neural network based multilayer perceptron can generate necessary knowledge for a knowledge-based model after the training process. Knowledge-based modeling improves the accuracy of the initial model to determine design parameters corresponding to the design target. Source difference, prior knowledge Input and prior knowledge input with difference can be applied to realize an efficient knowledge-based strategy. 3D-EM simulation generates the new model in terms of the design parameters of the proposed application. It has three switching states for operating, which are organized by two resistor circuits representing ON/OFF states. Switch positions and geometrical parameters can be used for satisfying design targets between 1 GHz and 6 GHz for the efficient antenna design.

Keywords

• Artificial neural networks
• Knowledge-based models
• Reconfigurable microstrip antenna
• Resistor circuits

References

1. [1] Bernhard J. Reconfigurable Antennas, California, USA, Morgan & Claypool Publishers, 2007.
2. [2] Li T, Zhai H, Wang X, Liang C. "Frequency-Reconfigurable bow-tie antenna for bluetooth, WiMAX, and WLAN applications". IEEE Antennas and Wireless Propagation Letters, 14, 171-174, 2014.
3. [3] Zhang B, Wang Y, Wang W, Tian Y. "On the downlink throughput capacity of hybrid wireless networks with MIMO". IEEE Accsess, 5, 26086-26091, 2017.
4. [4] Christodoulou C, Tawk Y, Lane S, Erwin S. "Reconfigurable antennas for wireless and space applications". Proceedings of the IEEE, 100(7), 2250-2261, 2012.
5. [5] Song T, Lee Y, Ga D, Choi J. "A polarization reconfigurable microstrip patch antenna using PIN diodes". 2012 IEEE Asia Pacific Microwave Conference Proceedings, Kaohsiung, Taiwan, 4-7 December 2012.
6. [6] Ismail M, Rahim M, Majid H. "The investigation of PIN diode switch on reconfigurable antenna". 2011 IEEE International RF & Microwave Conference, Negeri Sembilan, Malaysia, 12-14 December 2011.
7. [7] Khidre A, Lee K, Elsherbeni A. "Circular polarization reconfigurable wideband e-shaped patch antenna for wireless applications". IEEE Transactions on Antennas and Propagation, 61(2), 960-964, 2013.
8. [8] Chen R, Row J. "Single-fed microstrip patch antenna with switchable polarization". IEEE Transactions on Antennas and Propagation, 56(4), 922-926, 2008.

9. [9] Aoad A, Simsek M, Aydin Z. "Design of a reconfigurable 5-fingers shaped microstrip patch antenna by artificial neural networks". International Journal of Advanced Research in Computer Science and Software Engineering, 4(10), 61-70, 2014.
10. [10] Zhang Q, Gupta K. Neural Netwoks for RF and Microwave Design. Boston/London, UK, Artech House, 2000.
11. [11] Zhang Q, Wang F. "Knowledge-Based neural models for microwave design". IEEE Transaction on Microwave Theory and Techniques, 45(12), 2333-2343 1997.
12. [12] Watson P, Gupta K, Mahajan R. "Development of knowledge based artificial neural network models for microwave components". 1998 IEEE MTT-S International Microwave Symposium Digest, Baltimore, MD, USA, 7-12 June 1998.
13. [13] Simsek M, Zhang Q, Kabir H, Sengor N. "The recent Developments in Knowledge Based Neural Modelling". ELSEVIER Procedia Computer Scince, 1(1), 1321-1330, 2010.
14. [14] Chen Y, Taian Y, Qiang Z, Xu L. "Optimisation of reflection coefficient of microstrip antennas based on KBNN exploiting model". IET Microwaves, Antennas & Propagation, 12(4), 602-606, 2018.