Yusuke Koda | DeepAI

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∙ 12/17/2021

Communication-oriented Model Fine-tuning for Packet-loss Resilient Distributed Inference under Highly Lossy IoT Networks

The distributed inference (DI) framework has gained traction as a techni...

14 Sohei Itahara, et al. ∙

∙ 10/29/2021

Frame-Capture-Based CSI Recomposition Pertaining to Firmware-Agnostic WiFi Sensing

With regard to the implementation of WiFi sensing agnostic according to ...

0 Ryosuke Hanahara, et al. ∙

∙ 07/10/2021

Computer Vision-assisted Single-antenna and Single-anchor RSSI Localization Harnessing Dynamic Blockage Events

This paper demonstrates the feasibility of single-antenna and single-RF ...

0 Tomoya Sunami, et al. ∙

∙ 05/02/2021

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

Wireless channels can be inherently privacy-preserving by distorting the...

123 Yusuke Koda, et al. ∙

∙ 02/16/2021

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

This paper discusses the opportunity of bringing the concept of zero-sho...

0 Masao Shinzaki, et al. ∙

∙ 12/02/2020

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

This paper discusses the feasibility of beam tracking against dynamics i...

0 Yusuke Koda, et al. ∙

∙ 10/13/2020

When Wireless Communications Meet Computer Vision in Beyond 5G

This article articulates the emerging paradigm, sitting at the confluenc...

0 Takayuki Nishio, et al. ∙

∙ 08/14/2020

Distillation-Based Semi-Supervised Federated Learning for Communication-Efficient Collaborative Training with Non-IID Private Data

This study develops a federated learning (FL) framework overcoming large...

0 Sohei Itahara, et al. ∙

∙ 07/16/2020

Distributed Heteromodal Split Learning for Vision Aided mmWave Received Power Prediction

The goal of this work is the accurate prediction of millimeter-wave rece...

0 Yusuke Koda, et al. ∙

∙ 04/14/2020

Differentially Private AirComp Federated Learning with Power Adaptation Harnessing Receiver Noise

Over-the-air computation (AirComp)-based federated learning (FL) enables...

0 Yusuke Koda, et al. ∙

∙ 04/02/2020

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

This paper proposes a robust adversarial reinforcement learning (RARL)-b...

0 Yuto Kihira, et al. ∙

∙ 03/02/2020

Communication-Efficient Multimodal Split Learning for mmWave Received Power Prediction

The goal of this study is to improve the accuracy of millimeter wave rec...

0 Yusuke Koda, et al. ∙

∙ 11/05/2019

One Pixel Image and RF Signal Based Split Learning for mmWave Received Power Prediction

Focusing on the received power prediction of millimeter-wave (mmWave) ra...

0 Yusuke Koda, et al. ∙

∙ 06/12/2019

Cooperative Sensing in Deep RL-Based Image-to-Decision Proactive Handover for mmWave Networks

For reliable millimeter-wave (mmWave) networks, this paper proposes coop...

0 Yusuke Koda, et al. ∙

∙ 04/09/2019

End-to-End Learning of Proactive Handover Policy for Camera-Assisted mmWave Networks Using Deep Reinforcement Learning

For mmWave networks, this paper proposes an image-to-decision proactive ...

0 Yusuke Koda, et al. ∙

∙ 03/26/2018

Machine-Learning-Based Future Received Signal Strength Prediction Using Depth Images for mmWave Communications

This paper discusses a machine-learning (ML)-based future received signa...

0 Hironao Okamoto, et al. ∙

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