We introduce deep joint source-channel coding (DeepJSCC) schemes for image transmission over cooperative relay channels. The relay either amplifies-and-forwards its received signal, called DeepJSCC-AF, or leverages neural networks to extract relevant features from its received signal, called DeepJSCC-PF (Process-and-Forward). We consider both half- and full-duplex relays, and propose a novel transformer-based model at the relay. For a half-duplex relay, it is shown that the proposed scheme learns to generate correlated signals at the relay and source to obtain beamforming gains. In the full-duplex case, we introduce a novel block-based transmission strategy, in which the source transmits in blocks, and the relay updates its knowledge about the input signal after each block and generates its own signal. To enhance practicality, a single transformer-based model is used at the relay at each block, together with an adaptive transmission module, which allows the model to seamlessly adapt to different channel qualities and the transmission powers}. Simulation results demonstrate the superior performance of DeepJSCC-PF compared to the state-of-the-art BPG image compression algorithm operating at the maximum achievable rate of conventional decode-and-forward and compress-and-forward protocols, in both half- and full-duplex relay scenarios over AWGN and Rayleigh fading channels.

翻译：本文针对协作中继信道中的图像传输，提出了深度联合信源信道编码方案。中继节点可采用放大转发其接收信号（称为DeepJSCC-AF），或利用神经网络从接收信号中提取相关特征（称为DeepJSCC-PF，即过程与转发）。我们同时考虑了半双工与全双工中继场景，并提出了一种基于Transformer的新型中继模型。对于半双工中继，研究表明所提方案能够学习生成中继与信源间的相关信号以获得波束成形增益。在全双工场景中，我们提出了一种新颖的基于分块的传输策略：信源以分块形式传输，中继在每个分块后更新其对输入信号的认知并生成自身信号。为提升实用性，我们在每个传输分块的中继端采用单一Transformer模型，并配合自适应传输模块，使模型能够无缝适应不同信道质量与发射功率。仿真结果表明，在AWGN与瑞利衰落信道下的半双工及全双工中继场景中，DeepJSCC-PF方案相较于在传统解码转发与压缩转发协议最大可达速率下运行的先进BPG图像压缩算法，均展现出更优越的性能。