Capacity-Driven Autoencoders for Communications
The autoencoder concept has fostered the reinterpretation and the design of modern communication systems. It consists of an encoder, a channel and a decoder block that modify their internal neural structure in an end-to-end learning fashion. However, the current approach to train an autoencoder reli...
Main Authors: | Nunzio A. Letizia, Andrea M. Tonello |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2021-01-01
|
Series: | IEEE Open Journal of the Communications Society |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9449919/ |
Similar Items
-
Information Flows of Diverse Autoencoders
by: Sungyeop Lee, et al.
Published: (2021-07-01) -
Machine Learning Tips and Tricks for Power Line Communications
by: Andrea M. Tonello, et al.
Published: (2019-01-01) -
Basics of communications and coding/
by: 228621 Chambers, William G.
Published: (1985) -
Mutual Information-Weighted Principle Components Identified From the Depth Features of Stacked Autoencoders and Original Variables for Oil Dry Point Soft Sensor
by: Jie Wang, et al.
Published: (2019-01-01) -
Deep Learning-Based Autoencoder for m-User Wireless Interference Channel Physical Layer Design
by: Dehao Wu, et al.
Published: (2020-01-01)