M to 1 Joint Source-Channel Coding of Gaussian Sources via Dichotomy of the Input Space Based on Deep Learning

Abstract

In this paper, we propose a deep neural network framework for Joint Source-Channel Coding of an m dimensional i.i.d. Gaussian source for transmission over a single additive white Gaussian noise channel with no delay. The framework employs two neural encoder-decoder pairs that learn to split the input signal space into two disjoint support sets. The encoder and the decoder are jointly trained to minimize the mean square error subject to a power constraint on the signal transmitted across the channel. The proposed method achieves results as good as the state of the art for m=3,4 and is easily extendable to higher dimensions. The trained model, we discovered, assigns almost equal probability to the disjoint support sets. The results show that the scheme performance is within 1.9dB of the Shannon optimal limit over a wide range of Channel Signal to Noise Ratios (CSNR) from 0dB to 30dB for various values of m. The method is also robust, i.e. employing a model trained at CSNR+/-5dB is only 0.6dB worse than a model trained specifically for that CSNR.