351 citations · 730 across the 32 of their papers we have counts for
7 papers · 1 filter
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…
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.…