Toshihide Higashimori
Advanced Telecommunications Research Institute International
Papers
1
Total Citations
2
H-Index
1
About
Toshihide Higashimori is a researcher whose work sits 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 predicting received signal strength (RSS) in complex factory environments. By applying matrix factorization (MF) to interpolate sparse RSS measurements from a transmitter mounted on a moving robot, Higashimori addresses a critical challenge in enabling reliable wireless communication in industrial settings. This work contributes to the growing field of machine learning-based channel prediction, offering a practical solution for real-time radio environment estimation. While his citation count is still emerging, the technical depth and practical relevance of his research position him as a promising contributor to next-generation wireless systems, particularly for smart factories and autonomous robotics. Higashimori’s work exemplifies how data-driven methods can enhance the robustness of wireless links in dynamic, interference-prone environments.
Research Focus
Key Achievements
Top Papers
- 1