Mutual Coupling Reduction of Cross-Dipole Antenna for Base Stations by Using a Neural Network Approach
Abstract
:1. Introduction
2. Neural Network Approach
3. Cross-Dipole Antenna Design
4. Reduction of Mutual Coupling by a Neural Network Approach
5. Experimental Investigation and Results
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Variables | Output1 | Output2 | Outpu3 Isolation | |||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
X1 | X2 | X3 | X4 | X5 | X6 | Y1 | Y2 | Y3 | Y4 | Y5 | Y6 | Z1 | Z2 | Z3 | Z4 | Z5 | Z6 | S11 | S22 | S12 |
0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | −20.64 | −14.82 | −14.98 |
0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | −30.78 | −10.59 | −15.89 |
0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | −27.15 | −10.56 | −15.90 |
0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | −13.73 | −9.77 | −15.43 |
0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | −7.84 | −9.68 | −15.51 |
0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | −4.64 | −9.67 | −15.86 |
0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | −3.26 | −9.12 | −16.11 |
0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | −3.78 | −8.38 | −16.19 |
0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | −4.78 | −6.74 | −16.51 |
0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | −5.58 | −6.09 | −16.61 |
0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | −5.62 | −5.96 | −16.64 |
0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | −4.96 | −5.86 | −16.80 |
1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | −4.01 | −5.23 | −16.81 |
1 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | −4.35 | −4.77 | −16.81 |
1 | 1 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | −4.94 | −3.97 | −16.88 |
1 | 1 | 1 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | −5.35 | −3.60 | −16.85 |
1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | −5.33 | −3.43 | −16.76 |
1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | −4.90 | −3.24 | −16.70 |
1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 1 | −4.77 | −3.22 | −15.94 |
1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 1 | −4.66 | −3.30 | −15.78 |
1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 1 | −3.87 | −3.40 | −15.49 |
1 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 1 | −3.14 | −3.75 | −15.21 |
1 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 1 | −1.24 | −4.61 | −15.33 |
1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 1 | −1.24 | −5.00 | −15.77 |
1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 1 | −19.42 | −5.94 | −16.23 |
1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 1 | −20.90 | −6.34 | −16.28 |
1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | −20.00 | −6.43 | −16.60 |
1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | −19.83 | −6.53 | −16.67 |
1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | −20.49 | −4.62 | −16.90 |
1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | −21.16 | −4.88 | −17.02 |
1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | −21.57 | −4.90 | −17.06 |
0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | −21.83 | −4.90 | −17.09 |
0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | −21.27 | −13.09 | −16.27 |
1 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | −21.28 | −13.11 | −16.13 |
1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | −11 | −12.82 | −16.34 |
1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | −5.35 | −16.78 | −16.50 |
1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | −1.9 | −1.95 | −13.31 |
1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | −5.47 | −12.17 | −16.46 |
1 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | −19.75 | −12.55 | −16.41 |
1 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | −24.53 | −9.74 | −15.79 |
1 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | −1.57 | −1.22 | −10.45 |
0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | −25.10 | −9.73 | −15.75 |
0 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | −20.95 | −9.79 | −15.71 |
0 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | −7.63 | −9.73 | −16.26 |
0 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | −7.76 | −7.11 | −15.20 |
0 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | −8.27 | −1.64 | −14.89 |
0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | −27.53 | −9.88 | −15.59 |
1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | −27.01 | −9.87 | −15.63 |
X1 | X2 | X3 | X4 | X5 | X6 | Y1 | Y2 | Y3 | Y4 | Y5 | Y6 | Z1 | Z2 | Z3 | Z4 | Z5 | Z6 | Artificial Neural Network | FIT Based Simulator | % Error |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | −15.2782 | −15 | 1.85 |
0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | −15.8851 | −15.9 | 0.09 |
0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | −15.9021 | −15.9 | 0.01 |
0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | −15.4356 | −15.4 | 0.23 |
0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | −15.5183 | −15.5 | 0.11 |
1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 1 | −15.7793 | −15.8 | 0.13 |
1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | −16.6678 | −16.9 | 1.37 |
0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | −16.6741 | −16.4 | 1.67 |
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Share and Cite
Ozdemir, E.; Akgol, O.; Ozkan Alkurt, F.; Karaaslan, M.; Abdulkarim, Y.I.; Deng, L. Mutual Coupling Reduction of Cross-Dipole Antenna for Base Stations by Using a Neural Network Approach. Appl. Sci. 2020, 10, 378. https://doi.org/10.3390/app10010378
Ozdemir E, Akgol O, Ozkan Alkurt F, Karaaslan M, Abdulkarim YI, Deng L. Mutual Coupling Reduction of Cross-Dipole Antenna for Base Stations by Using a Neural Network Approach. Applied Sciences. 2020; 10(1):378. https://doi.org/10.3390/app10010378
Chicago/Turabian StyleOzdemir, Ersin, Oguzhan Akgol, Fatih Ozkan Alkurt, Muharrem Karaaslan, Yadgar I. Abdulkarim, and Lianwen Deng. 2020. "Mutual Coupling Reduction of Cross-Dipole Antenna for Base Stations by Using a Neural Network Approach" Applied Sciences 10, no. 1: 378. https://doi.org/10.3390/app10010378
APA StyleOzdemir, E., Akgol, O., Ozkan Alkurt, F., Karaaslan, M., Abdulkarim, Y. I., & Deng, L. (2020). Mutual Coupling Reduction of Cross-Dipole Antenna for Base Stations by Using a Neural Network Approach. Applied Sciences, 10(1), 378. https://doi.org/10.3390/app10010378