351 citations · 730 across the 32 of their papers we have counts for
9 papers · 1 filter
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…
Respiratory Rate Estimation Based on WiFi Frame Capture
T. Kanda, T. Sato, H. Awano +2
This paper presents a method that estimates the respiratory rate based on the frame capturing of wireless local area networks. The method uses beamforming feedback matrices (BFMs)…
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…
Packet-Loss-Tolerant Split Inference for Delay-Sensitive Deep Learning in Lossy Wireless Networks
Sohei Itahara, Takayuki Nishio, Koji Yamamoto
The distributed inference framework is an emerging technology for real-time applications empowered by cutting-edge deep machine learning (ML) on resource-constrained Internet of 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…
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…