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 Autoencoder (VAE). We show that when the source dimension is greater than the channel dimension, the ...