  We consider low-latency image transmission over a noisy wireless channel when
correlated side information is present only at the receiver side (the Wyner-Ziv
scenario). In particular, we are interested in developing practical schemes
using a data-driven joint source-channel coding (JSCC) approach, which has been
previously shown to outperform conventional separation-based approaches in the
practical finite blocklength regimes, and to provide graceful degradation with
channel quality. We propose a novel neural network architecture that
incorporates the decoder-only side information at multiple stages at the
receiver side. Our results demonstrate that the proposed method succeeds in
integrating the side information, yielding improved performance at all channel
conditions in terms of the various quality measures considered here, especially
at low channel signal-to-noise ratios (SNRs) and small bandwidth ratios (BRs).
We have made the source code of the proposed method public to enable further
research, and the reproducibility of the results.
