This paper introduces an innovative deep joint source-channel coding (DeepJSCC) approach to image transmission over a cooperative relay channel. The relay either amplifies and forwards a scaled version of its received signal, referred to as DeepJSCC-AF, or leverages neural networks to extract relevant features about the source signal before forwarding it to the destination, which we call DeepJSCC-PF (Process-and-Forward). In the full-duplex scheme, inspired by the block Markov coding (BMC) concept, we introduce a novel block transmission strategy built upon novel vision transformer architecture. In the proposed scheme, the source transmits information in blocks, and the relay updates its knowledge about the input signal after each block and generates its own signal to be conveyed to the destination. To enhance practicality, we introduce an adaptive transmission model, which allows a single trained DeepJSCC model to adapt seamlessly to various channel qualities, making it a versatile solution. Simulation results demonstrate the superior performance of our proposed DeepJSCC compared to the state-of-the-art BPG image compression algorithm, even when operating at the maximum achievable rate of conventional decode-and-forward and compress-and-forward protocols, for both half-duplex and full-duplex relay scenarios.

翻译：本文提出了一种创新的深度联合信源信道编码（DeepJSCC）方法，用于协作中继信道中的图像传输。中继要么放大并转发其接收信号的缩放版本（称为DeepJSCC-AF），要么利用神经网络在将信号转发至目的地之前提取源信号的相关特征（称为DeepJSCC-PF，即过程转发）。在全双工方案中，受块马尔可夫编码（BMC）概念的启发，我们引入了一种基于新型视觉变换器架构的块传输策略。在所提方案中，源节点以块形式传输信息，中继节点在每个块后更新对输入信号的认知，并生成自身信号以传递至目的地。为增强实用性，我们提出了一种自适应传输模型，使单个训练好的DeepJSCC模型能够无缝适应不同信道质量，成为一种通用解决方案。仿真结果表明，与传统解码转发和压缩转发协议在最大可达速率下运行相比，我们提出的DeepJSCC在半双工和中继全双工场景中均展现出优于最先进的BPG图像压缩算法的性能。