Joint Source-Channel Coding for Gaussian Sources over AWGN Channels using Variational Autoencoders

In this paper, we study joint source-channel coding of gaussian sources over multiple AWGN channels where the source dimension is greater than the number of channels. We model our system as a Variational Autoencoder and show that its loss function takes up a form that is an upper bound on the optimization function got from rate-distortion theory. The constructed system employs two encoders that learn to split the source input space into almost half with no constraints. The system is jointly trained in a data-driven manner, end-to-end. We achieve state of the art results for certain configurations, some of which are 0.7dB better than previous works. We also showcase that the trained encoder/decoder is robust, i.e., even if the channel conditions change by +/-5dB, the performance of the system does not vary by more than 0.7dB w.r.t. a system trained at that channel condition. The trained system, to an extent, has the ability to generalize when a single input dimension is dropped and for some scenarios it is less than 1dB away from the system trained for that reduced dimension.