Norisato Suga
Papers
1
Total Citations
2
H-Index
1
About
Norisato Suga is a researcher whose work lies at the intersection of wireless communications and machine learning, with a particular focus on radio environment mapping and channel prediction. His most-cited paper, "Matrix Factorization-Based RSS Interpolation for Radio Environment Prediction" (2021), introduces a novel approach to estimating received signal strength (RSS) in complex, dynamic settings such as factory floors. By applying matrix factorization to sparse RSS measurements from a transmitter mounted on a moving robot, Suga’s method enables accurate interpolation of the radio environment without requiring dense sampling. This work addresses a critical challenge in reliable wireless communication for industrial automation and robotics. While his citation count is still growing—reflecting the emerging nature of this research area—his contribution is notable for bridging signal processing and machine learning to solve practical deployment problems. Suga’s approach offers a computationally efficient alternative to traditional spatial interpolation techniques, making it particularly valuable for real-time applications in smart factories and IoT networks. His ongoing research continues to explore data-driven methods for spectrum sensing and wireless system optimization.
Research Focus
Key Achievements
Top Papers
- 1