activity
20182021
most citedOne Pixel Image and RF Signal Based Split Learning for mmWave Received Power Prediction

15 citations · 26 across the 5 of their papers we have counts for

collaborators

11 papers

cs.NI2021

AirMixML: Over-the-Air Data Mixup for Inherently Privacy-Preserving Edge Machine Learning

Yusuke Koda, Jihong Park, Mehdi Bennis +2

Wireless channels can be inherently privacy-preserving by distorting the received signals due to channel noise, and superpositioning multiple signals over-the-air. By harnessing th…

cs.LG2021

Zero-Shot Adaptation for mmWave Beam-Tracking on Overhead Messenger Wires through Robust Adversarial Reinforcement Learning

Masao Shinzaki, Yusuke Koda, Koji Yamamoto +5

Millimeter wave (mmWave) beam-tracking based on machine learning enables the development of accurate tracking policies while obviating the need to periodically solve beam-optimizat…

cs.NI20202 cited

Millimeter Wave Communications on Overhead Messenger Wire: Deep Reinforcement Learning-Based Predictive Beam Tracking

Yusuke Koda, Masao Shinzaki, Koji Yamamoto +5

This paper discusses the feasibility of beam tracking against dynamics in millimeter wave (mmWave) nodes placed on overhead messenger wires, including wind-forced perturbations and…

cs.NI20206 cited

Distributed Heteromodal Split Learning for Vision Aided mmWave Received Power Prediction

Yusuke Koda, Jihong Park, Mehdi Bennis +3

The goal of this work is the accurate prediction of millimeter-wave received power leveraging both radio frequency (RF) signals and heterogeneous visual data from multiple distribu…

cs.NI2020

Differentially Private AirComp Federated Learning with Power Adaptation Harnessing Receiver Noise

Yusuke Koda, Koji Yamamoto, Takayuki Nishio +1

Over-the-air computation (AirComp)-based federated learning (FL) enables low-latency uploads and the aggregation of machine learning models by exploiting simultaneous co-channel tr…

cs.NI2020

Adversarial Reinforcement Learning-based Robust Access Point Coordination Against Uncoordinated Interference

Yuto Kihira, Yusuke Koda, Koji Yamamoto +2

This paper proposes a robust adversarial reinforcement learning (RARL)-based multi-access point (AP) coordination method that is robust even against unexpected decentralized operat…