Automated top-down pruning optimization approach in RF power amplifier designs
Author:
Funder
Istanbul Teknik Üniversitesi
Publisher
Springer Science and Business Media LLC
Subject
Surfaces, Coatings and Films,Hardware and Architecture,Signal Processing
Link
https://link.springer.com/content/pdf/10.1007/s10470-020-01730-w.pdf
Reference28 articles.
1. Kouhalvandi, L., Ceylan, O., & Ozoguz, S. (2019). Automated matching network modeling and optimization for power amplifier designs. In 2019 11th International Conference on Electrical and Electronics Engineering (ELECO), pp. 510–513, https://doi.org/10.23919/ELECO47770.2019.8990407.
2. Kouhalvandi, L., Pirola, M., & Ozoguz, S. (2020). Automated two-step power amplifier design with pre-constructed artificial neural network. In 2020 43rd International Conference on Telecommunications and Signal Processing (TSP), pp. 617–620.
3. Kouhalvandi, L., Ceylan, O., & Ozoguz, S. (2020). Automated deep neural learning-based optimization for high performance high power amplifier designs. In IEEE Transactions on Circuits and Systems I: Regular Papers pp. 1–14.
4. Kouhalvandi, L., Ceylan, O., & Ozoguz, S. (2019). A review on optimization methods for designing RF power amplifiers, In 2019 11th International Conference on Electrical and Electronics Engineering (ELECO), pp. 375–378.
5. Rutenbar, R. A., Gielen, G. G. E., & Roychowdhury, J. (2007). Hierarchical modeling, optimization, and synthesis for system-level analog and RF designs. Proceedings of the IEEE, 95(3), 640–669. https://doi.org/10.1109/JPROC.2006.889371.
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