Koya Sato

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Assistant Professor

University of Tokyo

Japan

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About

Dr. Koya Sato is an Assistant Professor at the Tokyo University of Science, Japan. His research focuses on advanced topics in communications and IoT systems, including over-the-air computation, Bayesian optimization for beamforming design, and radio map estimation using RGB-D sensors. He has contributed significantly to the application of spatial regression frameworks and reinforcement learning algorithms in massive IoT networks.

Recent Grants

Grant: Close

SICORP, EIG CONCERT-Japan "ICT for Resilient, Safe and Secure Society"

Open Date: 2021-04-01

Close Date:

Grant: Close

Grant-in-Aid for Early-Career Scientists

Open Date: 2019-04-01

Close Date: 2022-03-01

Grant: Close

Grant-in-Aid for JSPS Fellows

Open Date: 2017-04-01

Close Date: 2018-03-01

Articles (16)

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2025

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2022

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2021

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2021

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2021

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2021

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2021

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2021

Collaborators (3)

Daisuke Sugimura

Tokyo Metropolitan University

JAPAN

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Koji Ishibashi

Professor

The University of Electro-Communications

JAPAN

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Koichi Adachi

The University of Electro-Communications

JAPAN

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Koya Sato

Research Interests

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Contact this professor

About

Recent Grants

Articles (16)

Adaptively Weighted Averaging Over-the-Air Computation and Its Application to Distributed Gaussian Process Regression

Bayesian Optimization Aided Low-Complexity Beamforming Design for Over-the-Air-Computing

Fast Reflective Beamforming With Situational Awareness in IRS Assisted Communication Systems

RGB-D Sensor-Aided Radio Map Estimation Using Materials Classification

A Location-Blind Spatial Regression Framework for IoT Monitoring Systems Based on Location Distribution and Spatial Correlation

Bayesian Optimization Framework for Channel Simulation-Based Base Station Placement and Transmission Power Design

Indoor Radio Map Construction via Ray Tracing With RGB-D Sensor-Based 3D Reconstruction: Concept and Experiments in WLAN Systems

A Communication-Efficient Secure Routing Protocol for IoT Networks

Performance Evaluation of Reinforcement Learning Based Distributed Channel Selection Algorithm in Massive IoT Networks

Radio Map Extrapolation Using Compensated Empirical CDF Under Interference-Limited Observations

A High-Speed Channel Assignment Algorithm for Dense IEEE 802.11 Systems via Coherent Ising Machine

A High-Speed Channel Assignment Algorithm for Dense IEEE 802.11 Systems via Coherent Ising Machine

Rate-Adapted Decentralized Learning Over Wireless Networks

Rate-Adapted Decentralized Learning Over Wireless Networks

Image-Driven Spatial Interpolation with Deep Learning for Radio Map Construction

Image-Driven Spatial Interpolation With Deep Learning for Radio Map Construction

Collaborators (3)

Daisuke Sugimura

Koji Ishibashi

Koichi Adachi

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