351 citations · 730 across the 32 of their papers we have counts for
20 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…
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…
Decentralized and Model-Free Federated Learning: Consensus-Based Distillation in Function Space
Akihito Taya, Takayuki Nishio, Masahiro Morikura +1
This paper proposes a fully decentralized federated learning (FL) scheme for Internet of Everything (IoE) devices that are connected via multi-hop networks. Because FL algorithms h…
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…
MAB-based Client Selection for Federated Learning with Uncertain Resources in Mobile Networks
Naoya Yoshida, Takayuki Nishio, Masahiro Morikura +1
This paper proposes a client selection method for federated learning (FL) when the computation and communication resource of clients cannot be estimated; the method trains a machin…
Online Trainable Wireless Link Quality Prediction System using Camera Imagery
Sohei Itahara, Takayuki Nishio, Masahiro Morikura +1
Machine-learning-based prediction of future wireless link quality is an emerging technique that can potentially improve the reliability of wireless communications, especially at hi…
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…
Transfer Learning-Based Received Power Prediction with Ray-tracing Simulation and Small Amount of Measurement Data
Masahiro Iwasaki, Takayuki Nishio, Masahiro Morikura +1
This paper proposes a method to predict received power in urban area deterministically, which can learn a prediction model from small amount of measurement data by a simulation-aid…
Lottery Hypothesis based Unsupervised Pre-training for Model Compression in Federated Learning
Sohei Itahara, Takayuki Nishio, Masahiro Morikura +1
Federated learning (FL) enables a neural network (NN) to be trained using privacy-sensitive data on mobile devices while retaining all the data on their local storages. However, FL…
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…
Random Access with Opportunity Detection in Wireless Networks
Jinho Choi, Seung-Woo Ko, Koji Yamamoto +1
This letter proposes a novel random medium access control (MAC) based on a transmission opportunity prediction, which can be measured in a form of a conditional success probability…
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…
Concurrent Transmission Scheduling for Perceptual Data Sharing in mmWave Vehicular Networks
Akihito Taya, Takayuki Nishio, Masahiro Morikura +1
Sharing perceptual data with other vehicles enhances the traffic safety of autonomous vehicles because it helps vehicles locate other vehicles and pedestrians in their blind spots.…
Deep-Reinforcement-Learning-Based Distributed Vehicle Position Controls for Coverage Expansion in mmWave V2X
Akihito Taya, Takayuki Nishio, Masahiro Morikura +1
In millimeter wave (mmWave) vehicular communications, multi-hop relay disconnection by line-of-sight (LOS) blockage is a critical problem, especially in the early diffusion phase o…
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…