Numerical Comparison of Supervised Machine Learning-Based On-Off Encoded Eigenvalue Demodulation Methods
This paper employs representative machine learning techniques on on-off encoded eigenvalue demodulation and investigates the optimum in terms of BER performance. Simulation results indicated a random forest technique is the best in the 10-eigenvalue case. © 2024 The Author(s)
Authors
- Ken Mishina (ORCID: https://orcid.org/0000-0001-9460-3870)
- Daisuke Hisano (ORCID: https://orcid.org/0000-0002-5766-3812)
- Akihiro Maruta (ORCID: https://orcid.org/0000-0001-7503-5097)
- Kohei Nishida
- Juan David Ariza Cabrera
Institutions
- The University of Osaka (JP)
Publication Details
- Published
- 2024-08-04
- DOI
- https://doi.org/10.1109/cleo-pr60912.2024.10676799
- Primary Topic
- Ultrasonics and Acoustic Wave Propagation
- Type
- article
- Field-Weighted Citation Impact
- 0.00