Edge Learning for B5G Networks With Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensing

To process and transfer large amounts of data in emerging wireless services, it has become increasingly appealing to exploit distributed data communication and learning. Specifically, edge learning (EL) enables local model training on geographically disperse edge nodes and minimizes the need for fre...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE journal of selected topics in signal processing Jg. 17; H. 1; S. 9 - 39
Hauptverfasser:	Xu, Wei, Yang, Zhaohui, Ng, Derrick Wing Kwan, Levorato, Marco, Eldar, Yonina C., Debbah, Merouane
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.01.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Artificial intelligence (AI) beyond 5G (B5G) Communication Communication networks communication optimization Communications networks Communications systems Data communication Data exchange Deep learning deep learning (DL) Edge computing edge learning (EL) Entropy (Information theory) federated learning (FL) Information theory Internet-of-Everything (IoE) Learning Mathematical models Measurement multi-agent reinforcement learning (MARL) Optimization Performance measurement Reinforcement learning Semantics Signal processing Signal processing algorithms Training Wireless communication Wireless communications Wireless networks
ISSN:	1932-4553, 1941-0484
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!