A Design of Adaptive Genetic Algorithm-Based Optimized Power Amplifier for 5G Applications
In this paper, a novel adaptive genetic algorithm (AGA) is presented for the optimization goals. The presented AGA is discussed and implemented on a power amplifier (PA), which is designed for 24 GHz and 5G applications and fabricated in a 65-nm CMOS process. The PA is optimized by the AGA for the h...
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| Published in | Circuits, systems, and signal processing Vol. 43; no. 1; pp. 2 - 21 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
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New York
Springer US
01.01.2024
Springer Nature B.V |
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| Online Access | Get full text |
| ISSN | 0278-081X 1531-5878 |
| DOI | 10.1007/s00034-023-02447-7 |
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| Abstract | In this paper, a novel adaptive genetic algorithm (AGA) is presented for the optimization goals. The presented AGA is discussed and implemented on a power amplifier (PA), which is designed for 24 GHz and 5G applications and fabricated in a 65-nm CMOS process. The PA is optimized by the AGA for the high power-added efficiency (PAE), i.e., optimum. In the AGA, we aimed to avoid the local optima and slow convergence rate that exist in the conventional genetic algorithm (CGA). In the AGA, we proposed a parameter tuning method to fine-tune the set of PA circuit component values for faster optimization of the PA to have a high PAE. The proposed AGA provides a significant speed in optimization process as compared to the CGA, and the execution time of the AGA is faster than that of the CGA. The proposed AGA is also verified, and its performance is compared with that of the CGA through multiple multidimensional mathematical benchmark functions. The PA performance parameters are measured, and the results showed that the optimized PA achieves a high gain of 29.9 dB. The
P
sat
of PA is measured as 14.21 dBm, and IIP3 is 13.8 dBm. The simulated result shows the optimized PA with the AGA has PAE of 49.7%, while that with the CGA has PAE of 47.5% at 24 GHz. The final measured PAE of AGA’s optimized PA is 47.1%. The chip area of PA is 0.29 mm
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| AbstractList | In this paper, a novel adaptive genetic algorithm (AGA) is presented for the optimization goals. The presented AGA is discussed and implemented on a power amplifier (PA), which is designed for 24 GHz and 5G applications and fabricated in a 65-nm CMOS process. The PA is optimized by the AGA for the high power-added efficiency (PAE), i.e., optimum. In the AGA, we aimed to avoid the local optima and slow convergence rate that exist in the conventional genetic algorithm (CGA). In the AGA, we proposed a parameter tuning method to fine-tune the set of PA circuit component values for faster optimization of the PA to have a high PAE. The proposed AGA provides a significant speed in optimization process as compared to the CGA, and the execution time of the AGA is faster than that of the CGA. The proposed AGA is also verified, and its performance is compared with that of the CGA through multiple multidimensional mathematical benchmark functions. The PA performance parameters are measured, and the results showed that the optimized PA achieves a high gain of 29.9 dB. The Psat of PA is measured as 14.21 dBm, and IIP3 is 13.8 dBm. The simulated result shows the optimized PA with the AGA has PAE of 49.7%, while that with the CGA has PAE of 47.5% at 24 GHz. The final measured PAE of AGA’s optimized PA is 47.1%. The chip area of PA is 0.29 mm2. In this paper, a novel adaptive genetic algorithm (AGA) is presented for the optimization goals. The presented AGA is discussed and implemented on a power amplifier (PA), which is designed for 24 GHz and 5G applications and fabricated in a 65-nm CMOS process. The PA is optimized by the AGA for the high power-added efficiency (PAE), i.e., optimum. In the AGA, we aimed to avoid the local optima and slow convergence rate that exist in the conventional genetic algorithm (CGA). In the AGA, we proposed a parameter tuning method to fine-tune the set of PA circuit component values for faster optimization of the PA to have a high PAE. The proposed AGA provides a significant speed in optimization process as compared to the CGA, and the execution time of the AGA is faster than that of the CGA. The proposed AGA is also verified, and its performance is compared with that of the CGA through multiple multidimensional mathematical benchmark functions. The PA performance parameters are measured, and the results showed that the optimized PA achieves a high gain of 29.9 dB. The P sat of PA is measured as 14.21 dBm, and IIP3 is 13.8 dBm. The simulated result shows the optimized PA with the AGA has PAE of 49.7%, while that with the CGA has PAE of 47.5% at 24 GHz. The final measured PAE of AGA’s optimized PA is 47.1%. The chip area of PA is 0.29 mm 2 . |
| Author | Ryu, Jee Youl Siddique, Abrar Delwar, Tahesin Samira Aras, Unal |
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| Cites_doi | 10.1109/JSSC.2005.848143 10.1109/JSSC.2021.3070800 10.1109/TMTT.2015.2495360 10.1109/TMTT.2016.2636146 10.1109/TMTT.2003.820904 10.1109/TMTT.2012.2189243 10.2299/jsp.22.243 10.1016/j.optcom.2018.05.088 10.1109/TMTT.2012.2220561 10.1155/2019/8640218 10.1016/j.vlsi.2021.12.004 10.1109/TMTT.2021.3061547 10.1109/TMTT.2014.2327919 10.1109/TMTT.2016.2570212 10.1007/s11277-015-2346-x 10.1109/TMTT.2003.809179 10.1109/LMWC.2015.2505651 10.1109/TMTT.2013.2287677 10.1109/TMTT.2020.3040385 10.1109/TCSI.2020.3008947 10.1109/TMTT.1982.1131411 10.1109/TMTT.2010.2078890 10.1109/63.974373 10.1109/LMWC.2011.2176475 10.1109/JSSC.2019.2899493 10.1109/TCSI.2019.2958366 10.1016/j.asoc.2019.105831 10.1201/9781420035568.ch11 10.1201/9781420035568.ch4 10.23919/ELECO47770.2019.8990396 |
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| Keywords | Conventional genetic algorithm Circuit optimization 5G Adaptive genetic algorithm Power amplifier |
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| SubjectTerms | Adaptive algorithms Circuits Circuits and Systems Design optimization Electrical Engineering Electronics and Microelectronics Engineering Functions (mathematics) Genetic algorithms High gain Instrumentation Mathematical analysis Optimization Parameters Power amplifiers Signal,Image and Speech Processing |
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| Title | A Design of Adaptive Genetic Algorithm-Based Optimized Power Amplifier for 5G Applications |
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