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Joint Source-Channel Coding of Gaussian sources over AWGN channels via Manifold Variational Autoencoders

In this paper, we give a new scheme for Joint Source-Channel Coding of Gaussian sources over AWGN channels. We present a novel VAE architecture that exploits the manifold nature of joint source-channel coding. Since the projection onto a complex manifold is a highly discontinuous function we split the encoder into two arms and a selector. This design combined with the power of data-driven deep neural networks allows us to efficiently train an encoder-decoder system end-to-end. The scheme achieves state of the art performance for five out of the seven configurations tested upon and match the state of the art for one of the other configurations for almost all channel conditions tested upon. Further, without providing any expert prior on the optimum JSCC encoder, the proposed method learns an encoding function that is similar to the ones proposed and found by the researchers in the past three decades via heuristic approaches or complex variational analysis for simple configurations. It is also able to find excellent encoders and decoders for those source channel dimensions where past works have been unable to find any good solutions. Finally, we show that the system is also robust to changes in channel conditions.