Machine Learning Innovations for Wireless Communications and Networking
Explore the latest issue of the TCCN Newsletter, focusing on machine learning topics vital for the TCCN community. Discusses Federated Learning, Reinforcement Learning, and Deep Learning for Wireless Communications and Networking. Discover the significance of decentralized ML approaches like federated learning and reinforcement learning in wireless communication applications. Get insights into upcoming position papers and feature editors in this exciting field.
Download Presentation
Please find below an Image/Link to download the presentation.
The content on the website is provided AS IS for your information and personal use only. It may not be sold, licensed, or shared on other websites without obtaining consent from the author. If you encounter any issues during the download, it is possible that the publisher has removed the file from their server.
You are allowed to download the files provided on this website for personal or commercial use, subject to the condition that they are used lawfully. All files are the property of their respective owners.
The content on the website is provided AS IS for your information and personal use only. It may not be sold, licensed, or shared on other websites without obtaining consent from the author.
E N D
Presentation Transcript
TCCN Newsletter Report Presentation at TCCN Online Meeting July 2021 Universit du Qu bec INRS-EMT IEEE ICC 2021
Introduction TCCN Newsletter is a bi-annual electronic platform dedicated to present and discuss some emerging topics related to TCCN areas of interest. Topics already covered (since December 2015): Ultra-Reliable Low-Latency Communications (URLLC) Massive Machine-Type Communications (mMTC) Unmanned aerial vehicles (UAV) Non-Orthogonal MultipleAccess (NOMA) mmWave Communications Blockchain Internet of Things Artificial Intelligence THz Communications Quantum Communications Reconfigurable Intelligent Surfaces Full-Duplex Communications Integrated Space/Aerial/Terrestrial Communication Universit du Qu bec INRS-EMT IEEE ICC 2021
New Issue July 2021 This new issue covers some topics in machine learning that are of extreme importance for the TCCN community. Federated Learning, Reinforcement Learning, and Deep Learning for Wireless Communications and Networking To be launched on August 2021- there is a pending position paper. Universit du Qu bec INRS-EMT IEEE ICC 2021
New Issue July 2021 Federated Learning for wireless communications and networking (to be completed) Conventional machine learning (ML) approaches rely on the assumption of having the data and processing heads in a central entity. However, this is not always feasible in wireless communications applications because of the inaccessibility of private data and large communication overhead required to transmit raw data. Thus, decentralized ML approaches such as federated learning that keep the data where it is generated are much more appealing. Feature Editor and Interviews: To be invited Two Position Papers: To be selected Universit du Qu bec INRS-EMT IEEE ICC 2021
New Issue July 2021 Reinforcement Learning for wireless communications and networking (to be completed) Modern networks, e.g., IoT and UAV networks, become more decentralized and autonomous, where network entities need to make decisions locally under uncertainty of network environment. Reinforcement learning has been efficiently used to enable the network entities to obtain the optimal policy. In large-scale complex networks, a combination of reinforcement learning with deep learning, i.e., deep reinforcement learning, has been proposed as an effective approach to find the optimal policy when the state and action spaces are very large. Feature Editor and Interviews: To be invited Two Position Papers: To be selected Universit du Qu bec INRS-EMT IEEE ICC 2021
Any question or suggestion, please contact the TCCN officers, Prof. Lin Gao email: gaolin021@hotmail.com Universit du Qu bec INRS-EMT IEEE ICC 2021
