351 citations · 591 across the 22 of their papers we have counts for
23 papers
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…
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…
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…
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…
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…
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…
Deep Reinforcement Learning-Based Channel Allocation for Wireless LANs with Graph Convolutional Networks
Kota Nakashima, Shotaro Kamiya, Kazuki Ohtsu +3
Last year, IEEE 802.11 Extremely High Throughput Study Group (EHT Study Group) was established to initiate discussions on new IEEE 802.11 features. Coordinated control methods of t…
Hybrid-FL for Wireless Networks: Cooperative Learning Mechanism Using Non-IID Data
Naoya Yoshida, Takayuki Nishio, Masahiro Morikura +2
This paper proposes a cooperative mechanism for mitigating the performance degradation due to non-independent-and-identically-distributed (non-IID) data in collaborative machine le…
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…
Asymptotic Analysis of Normalized SNR-Based Scheduling in Uplink Cellular Networks with Truncated Channel Inversion Power Control
Shotaro Kamiya, Koji Yamamoto, Seong-Lyun Kim +2
This paper provides the signal-to-interference-plus-noise ratio (SINR) complimentary cumulative distribution function (CCDF) and average data rate of the normalized SNR-based sched…
Stochastic Geometry Analysis of Normalized SNR-Based Scheduling in Downlink Cellular Networks
Takuya Ohto, Koji Yamamoto, Seong-Lyun Kim +2
The coverage probability and average data rate of normalized SNR-based scheduling in a downlink cellular network are derived by modeling the locations of the base stations and user…