Machine Learning-Ready Computer-Aided Butler Matrix Designer
With the rise of 5G, beamforming networks like the Butler Matrix are essential but challenging to design. This paper presents a Python-based, open-source Butler Matrix designer that generates STL files for electromagnetic tools. Unlike manual methods tied to specific software, this approach supports universal designs and integrates machine learning for inverse design, enabling users to create Butler matrices based on specified requirements efficiently.
Authors
- Kamakshi Mehta Ashu Tripathi
- Junwei Dong
- P. C. Pandey (ORCID: https://orcid.org/0000-0002-5951-5530)
- Prakhar Chaubey
- Ravi Kumar Arya (ORCID: https://orcid.org/0000-0003-0724-8060)
- Prashant Chaudhary (ORCID: https://orcid.org/0000-0003-3638-7860)
- Shiyuan Kong
- Nikhil Rawat (ORCID: https://orcid.org/0000-0003-4529-9280)
- Kartik Rana
- Harshit Mrutunjai
- Tushar Rajora
Institutions
- University of Central Florida (US)
- Changchun University of Science and Technology (CN)
- Defence Research and Development Organisation (IN)
- Government of Uttar Pradesh (IN)
Publication Details
- Published
- 2025-06-05
- DOI
- https://doi.org/10.1109/wams64402.2025.11158085
- Primary Topic
- Manufacturing Process and Optimization
- Type
- article
- Field-Weighted Citation Impact
- 0.00