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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Vydáno v:IEEE journal on selected areas in communications Ročník 39; číslo 7; s. 2000 - 2013
Hlavní autoři: Saidutta, Yashas Malur, Abdi, Afshin, Fekri, Faramarz
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.07.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
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
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Shrnutí: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 Autoencoder (VAE). We show that when the source dimension is greater than the channel dimension, the encoding of two source samples in the neighborhood of each other need not be near each other. Such discontinuous projection needs to be accounted for by using multiple encoders and selecting an encoder to encode samples on a particular side of the discontinuity. We explore two selection methodologies, one based on an intuitive rule and the other where it is posed as a learning task in a Mixture-of-Experts (MoE) setup. We analyze the gradients of these methods and reason why the latter is better at avoiding local optima. We show the efficacy of the proposed methodology by simulating the performance of the system for JSCC of Gaussian sources over AWGN channels and showing that the learned solutions are close to or better than the ones proposed earlier. The proposed methodology is also naturally capable of generalizing to other source distributions which we showcase by simulating for Laplace sources. The learned systems are also robust to changes in channel conditions. Further, a single system can be trained to generalize over a range of channel conditions provided the channel conditions are known at both the transmitter and the receiver. Finally, we evaluate our proposed methodology on three different image datasets and showcase consistent improvement over existing methods due to the VAE formulation.
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ISSN:0733-8716
1558-0008
DOI:10.1109/JSAC.2021.3078489