Joint Source-Channel Coding Over Additive Noise Analog Channels Using Mixture of Variational Autoencoders

In this paper, we present a learning scheme for Joint Source-Channel Coding (JSCC) over analog independent additive noise channels. We formulate the learning problem by showing that the minimization loss function from rate-distortion theory, is upper bounded by the loss function of the Variational A...

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Vydané v:	IEEE journal on selected areas in communications Ročník 39; číslo 7; s. 2000 - 2013
Hlavní autori:	Saidutta, Yashas Malur, Abdi, Afshin, Fekri, Faramarz
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.07.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	AWGN channels Channel coding Channels Coders Coding Cognitive tasks Decoding deep learning Discontinuity Joint source-channel coding Learning machine learning Methodology Neural networks Optimization Scalability Training Variational Autoencoders
ISSN:	0733-8716, 1558-0008
On-line prístup:	Získať plný text
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