Polarized Dropout: A Novel Deep Joint Source Channel Coding Scheme for Erasure Channels

This letter investigates the challenges encountered by deep joint source-channel coding in erasure channels. We explore the effectiveness of the widely adopted dropout technique in endowing deep neural networks with resilience against erasures. However, directly applying dropout at the channel layer...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE communications letters Jg. 28; H. 9; S. 1986 - 1990
Hauptverfasser:	Ren, Zichang, Wang, Yiru, Zhao, Yuping
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.09.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Artificial neural networks Channels Coding Decoding deep learning erasure channel Joint source-channel coding Number theory Performance degradation polar code Receivers Symbols Training Transfer functions Transforms Vectors
ISSN:	1089-7798, 1558-2558
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!