351 citations · 606 across the 20 of their papers we have counts for
21 papers
Vision-Aided Frame-Capture-Based CSI Recomposition for WiFi Sensing: A Multimodal Approach
Hiroki Shimomura, Yusuke Koda, Takamochi Kanda +3
Recompositing channel state information (CSI) from the beamforming feedback matrix (BFM), which is a compressed version of CSI and can be captured because of its lack of encryption…
Communication-oriented Model Fine-tuning for Packet-loss Resilient Distributed Inference under Highly Lossy IoT Networks
Sohei Itahara, Takayuki Nishio, Yusuke Koda +1
The distributed inference (DI) framework has gained traction as a technique for real-time applications empowered by cutting-edge deep machine learning (ML) on resource-constrained…
Bi-directional Beamforming Feedback-based Firmware-agnostic WiFi Sensing: An Empirical Study
S. Kondo, S. Itahara, K. Yamashita +4
In the field of WiFi sensing, as an alternative sensing source of the channel state information (CSI) matrix, the use of a beamforming feedback matrix (BFM)that is a right singular…
Frame-Capture-Based CSI Recomposition Pertaining to Firmware-Agnostic WiFi Sensing
Ryosuke Hanahara, Sohei Itahara, Kota Yamashita +4
With regard to the implementation of WiFi sensing agnostic according to the availability of channel state information (CSI), we investigate the possibility of estimating a CSI matr…
Beamforming Feedback-based Model-Driven Angle of Departure Estimation Toward Legacy Support in WiFi Sensing: An Experimental Study
Sohei Itahara, Sota Kondo, Kota Yamashita +3
This study experimentally validated the possibility of angle of departure (AoD) estimation using multiple signal classification (MUSIC) with only WiFi control frames for beamformin…
Computer Vision-assisted Single-antenna and Single-anchor RSSI Localization Harnessing Dynamic Blockage Events
Tomoya Sunami, Sohei Itahara, Yusuke Koda +2
This paper demonstrates the feasibility of single-antenna and single-RF (radio frequency)- anchor received power strength indicator (RSSI) localization (SARR-LOC) with the assistan…
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…
ACK-Less Rate Adaptation for IEEE 802.11bc Enhanced Broadcast Services Using Sim-to-Real Deep Reinforcement Learning
T. Kanda, Y. Koda, K. Yamamoto +1
In IEEE 802.11bc, the broadcast mode on wireless local area networks (WLANs), data rate control that is based on acknowledgement (ACK) mechanism similar to the one in the current I…
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…
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…
When Wireless Communications Meet Computer Vision in Beyond 5G
Takayuki Nishio, Yusuke Koda, Jihong Park +2
This article articulates the emerging paradigm, sitting at the confluence of computer vision and wireless communication, to enable beyond-5G/6G mission-critical applications (auton…
Distillation-Based Semi-Supervised Federated Learning for Communication-Efficient Collaborative Training with Non-IID Private Data
Sohei Itahara, Takayuki Nishio, Yusuke Koda +2
This study develops a federated learning (FL) framework overcoming largely incremental communication costs due to model sizes in typical frameworks without compromising model perfo…
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…
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…
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…
Communication-Efficient Multimodal Split Learning for mmWave Received Power Prediction
Yusuke Koda, Jihong Park, Mehdi Bennis +3
The goal of this study is to improve the accuracy of millimeter wave received power prediction by utilizing camera images and radio frequency (RF) signals, while gathering image in…
Penalized and Decentralized Contextual Bandit Learning for WLAN Channel Allocation with Contention-Driven Feature Extraction
Kota Yamashita, Shotaro Kamiya, Koji Yamamoto +3
In this study, a contextual multi-armed bandit (CMAB)-based decentralized channel exploration framework disentangling a channel utility function (i.e., reward) with respect to cont…
One Pixel Image and RF Signal Based Split Learning for mmWave Received Power Prediction
Yusuke Koda, Jihong Park, Mehdi Bennis +3
Focusing on the received power prediction of millimeter-wave (mmWave) radio-frequency (RF) signals, we propose a multimodal split learning (SL) framework that integrates RF receive…
Cooperative Sensing in Deep RL-Based Image-to-Decision Proactive Handover for mmWave Networks
Yusuke Koda, Koji Yamamoto, Takayuki Nishio +1
For reliable millimeter-wave (mmWave) networks, this paper proposes cooperative sensing with multi-camera operation in an image-to-decision proactive handover framework that direct…
Handover Management for mmWave Networks with Proactive Performance Prediction Using Camera Images and Deep Reinforcement Learning
Yusuke Koda, Kota Nakashima, Koji Yamamoto +2
For millimeter-wave networks, this paper presents a paradigm shift for leveraging time-consecutive camera images in handover decision problems. While making handover decisions, it…
Proactive Received Power Prediction Using Machine Learning and Depth Images for mmWave Networks
Takayuki Nishio, Hironao Okamoto, Kota Nakashima +5
This study demonstrates the feasibility of the proactive received power prediction by leveraging spatiotemporal visual sensing information toward the reliable millimeter-wave (mmWa…