Extreme ultra-reliable and low-latency communication

J Park, S Samarakoon, H Shiri, MK Abdel-Aziz… - Nature …, 2022 - nature.com
Ultra-reliable and low-latency communication (URLLC) is central to fifth-generation (5G)
communication systems, but the fundamentals of URLLC remain elusive. New immersive …

Wi-Fi meets ML: A survey on improving IEEE 802.11 performance with machine learning

S Szott, K Kosek-Szott, P Gawłowicz… - … Surveys & Tutorials, 2022 - ieeexplore.ieee.org
Wireless local area networks (WLANs) empowered by IEEE 802.11 (Wi-Fi) hold a dominant
position in providing Internet access thanks to their freedom of deployment and configuration …

A very brief introduction to machine learning with applications to communication systems

O Simeone - IEEE Transactions on Cognitive Communications …, 2018 - ieeexplore.ieee.org
Given the unprecedented availability of data and computing resources, there is widespread
renewed interest in applying data-driven machine learning methods to problems for which …

A review of deep learning in 5G research: Channel coding, massive MIMO, multiple access, resource allocation, and network security

A Ly, YD Yao - IEEE Open Journal of the Communications …, 2021 - ieeexplore.ieee.org
The current development of 5G technology is flourishing with widespread deployment
across the world at a rapid pace. However, there is still a demand concerning 5G research …

Link performance prediction technologies

J Svennebring, AV Jeyaraj - US Patent 11,159,408, 2021 - Google Patents
Various systems and methods for determining and communicating Link Performance
Predictions (LPPs), such as in connection with management of radio communication links …

Recent advances in mmWave-radar-based sensing, its applications, and machine learning techniques: A review

A Soumya, C Krishna Mohan, LR Cenkeramaddi - Sensors, 2023 - mdpi.com
Human gesture detection, obstacle detection, collision avoidance, parking aids, automotive
driving, medical, meteorological, industrial, agriculture, defense, space, and other relevant …

Handover management for mmWave networks with proactive performance prediction using camera images and deep reinforcement learning

Y Koda, K Nakashima, K Yamamoto… - IEEE Transactions …, 2019 - ieeexplore.ieee.org
For millimeter-wave networks, this paper presents a paradigm shift for leveraging time-
consecutive camera images in handover decision problems. While making handover …

When wireless communications meet computer vision in beyond 5G

T Nishio, Y Koda, J Park, M Bennis… - IEEE Communications …, 2021 - ieeexplore.ieee.org
This article articulates the emerging paradigm, sitting at the confluence of computer vision
and wireless communication, enabling beyond-5G/6G mission-critical applications …

AI-enabled reliable QoS in multi-RAT wireless IoT networks: Prospects, challenges, and future directions

K Zia, A Chiumento… - IEEE Open Journal of the …, 2022 - ieeexplore.ieee.org
Wireless IoT networks have seen an unprecedented rise in number of devices,
heterogeneity and emerging use cases which led to diverse throughput, reliability and …

Communication-efficient multimodal split learning for mmWave received power prediction

Y Koda, J Park, M Bennis, K Yamamoto… - IEEE …, 2020 - ieeexplore.ieee.org
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 …