351 citations · 606 across the 20 of their papers we have counts for
13 papers · 1 filter
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…
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…
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…
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…