Low-Delay Analog Joint Source-Channel Coding with Deep Learning

We consider the design of low-delay joint source-channel coding (JSCC) schemes for the transmission of discrete-time analog sources over noisy channels based on deep neural networks. The design problem is addressed as optimization of an autoencoder model, and several scenarios are discussed. For poi...

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Bibliographic Details
Published in:	IEEE transactions on communications Vol. 71; no. 1; p. 1
Main Authors:	Xuan, Ziwei, Narayanan, Krishna
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.01.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Artificial neural networks AWGN channels Channels Coders Coding Communication Decoding Deep learning Design optimization Distortion distributed source-channel coding Joint source-channel coding Machine learning Neural networks Noise measurement Random noise Receivers Recurrent neural networks RNNs SIRENs Training Transmission
ISSN:	0090-6778, 1558-0857
Online Access:	Get full text
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