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 noise levels in terms of the various distortion criteria considered here, especially at low channel signal-to-noise ratios (SNRs) and small bandwidth ratios (BRs). We also provide the source code of the proposed method to enable further research and reproducibility of the results.

翻译：我们考虑在仅接收端存在相关侧信息（Wyner-Ziv场景）时，通过噪声无线信道进行低延迟图像传输的问题。具体而言，我们致力于利用数据驱动的联合信源信道编码（JSCC）方法开发实用方案——该方法已被先前研究证明能够在实际有限分组长度范围内优于传统分离式方法，并随信道质量呈现平滑退化特性。我们提出一种新型神经网络架构，在接收端的多级处理过程中融入仅解码器侧信息。实验结果表明，所提出的方法成功整合了侧信息，在本文考虑的各种失真准则下，所有信道噪声水平（尤其是低信道信噪比和低带宽比区域）均实现了性能提升。我们还提供了所提出方法的源代码，以促进进一步研究并确保结果的可复现性。