Analog Joint Source-Channel Coding for Gaussian Sources over AWGN Channels with Deep Learning

We consider the design of neural network based joint source channel coding (JSCC) schemes for transmitting an independent and identically distributed (i.i. d.) Gaussian source over additive white Gaussian noise (AWGN) channels with bandwidth mismatch when the source dimension is small. Unlike existing deep learning based works on this topic, we do not resort to domain expertise to constrain the model; rather, we propose to employ fine tuning techniques to optimize the model. We show that our proposed techniques can provide performance that is comparable to that of the state-of-the-art when the source dimension is small. Furthermore, the proposed model can spontaneously learn encoding functions that are similar to those designed by conventional schemes. Finally, we empirically show that the learned JSCC scheme is robust to mismatch between the assumed and actual channel signal to noise ratios.