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.

翻译：我们考虑了在噪声无线信道上传输低延迟图像时，仅在接收端存在相关侧信息的情形（Wyner-Ziv场景）。特别地，我们致力于开发基于数据驱动的联合信源信道编码（JSCC）的实际方案，该方案在有限块长实际场景下已被证明优于传统的分离式方法，并能随信道质量提供优雅降级。我们提出了一种新型神经网络架构，该架构在接收端多阶段引入仅解码器侧信息。结果表明，所提方法成功整合了侧信息，在本文考虑的各种质量度量指标下，所有信道条件（特别是低信道信噪比和低带宽比）均获得性能提升。我们已公开提出方法的源代码，以促进进一步研究及结果的可重复性。