Distributed learning in wireless networks: Recent progress and future challenges
The next-generation of wireless networks will enable many machine learning (ML) tools and
applications to efficiently analyze various types of data collected by edge devices for …
applications to efficiently analyze various types of data collected by edge devices for …
Modeling, diagnostics, optimization, and control of internal combustion engines via modern machine learning techniques: A review and future directions
A critical review of the existing Internal Combustion Engine (ICE) modeling, optimization,
diagnosis, and control challenges and the promising state-of-the-art Machine Learning (ML) …
diagnosis, and control challenges and the promising state-of-the-art Machine Learning (ML) …
Edge artificial intelligence for 6G: Vision, enabling technologies, and applications
KB Letaief, Y Shi, J Lu, J Lu - IEEE Journal on Selected Areas …, 2021 - ieeexplore.ieee.org
The thriving of artificial intelligence (AI) applications is driving the further evolution of
wireless networks. It has been envisioned that 6G will be transformative and will …
wireless networks. It has been envisioned that 6G will be transformative and will …
Optimized power control design for over-the-air federated edge learning
Over-the-air federated edge learning (Air-FEEL) has emerged as a communication-efficient
solution to enable distributed machine learning over edge devices by using their data locally …
solution to enable distributed machine learning over edge devices by using their data locally …
Joint user association and resource allocation for wireless hierarchical federated learning with IID and non-IID data
In this work, hierarchical federated learning (HFL) over wireless multi-cell networks is
proposed for large-scale model training while preserving data privacy. However, the …
proposed for large-scale model training while preserving data privacy. However, the …
Quantum neural networks: Concepts, applications, and challenges
Quantum deep learning is a research field for the use of quantum computing techniques for
training deep neural networks. The research topics and directions of deep learning and …
training deep neural networks. The research topics and directions of deep learning and …
Quantum multi-agent reinforcement learning via variational quantum circuit design
In recent years, quantum computing (QC) has been getting a lot of attention from industry
and academia. Especially, among various QC research topics, variational quantum circuit …
and academia. Especially, among various QC research topics, variational quantum circuit …
Stereoscopic scalable quantum convolutional neural networks
As the noisy intermediate-scale quantum (NISQ) era has begun, a quantum neural network
(QNN) is definitely a promising solution to many problems that classical neural networks …
(QNN) is definitely a promising solution to many problems that classical neural networks …
A survey of beam management for mmWave and THz communications towards 6G
Communication in millimeter wave (mmWave) and even terahertz (THz) frequency bands is
ushering in a new era of wireless communications. Beam management, namely initial …
ushering in a new era of wireless communications. Beam management, namely initial …
Quantum multi-agent actor-critic networks for cooperative mobile access in multi-UAV systems
C Park, WJ Yun, JP Kim, TK Rodrigues… - IEEE Internet of …, 2023 - ieeexplore.ieee.org
This article proposes a novel algorithm, named quantum multiagent actor–critic networks
(QMACN) for autonomously constructing a robust mobile access system employing multiple …
(QMACN) for autonomously constructing a robust mobile access system employing multiple …