This paper introduces a vision transformer (ViT)-based deep joint source and channel coding (DeepJSCC) scheme for wireless image transmission over multiple-input multiple-output (MIMO) channels, denoted as DeepJSCC-MIMO. We consider DeepJSCC-MIMO for adaptive image transmission in both open-loop and closed-loop MIMO systems. The novel DeepJSCC-MIMO architecture surpasses the classical separation-based benchmarks with robustness to channel estimation errors and showcases remarkable flexibility in adapting to diverse channel conditions and antenna numbers without requiring retraining. Specifically, by harnessing the self-attention mechanism of ViT, DeepJSCC-MIMO intelligently learns feature mapping and power allocation strategies tailored to the unique characteristics of the source image and prevailing channel conditions. Extensive numerical experiments validate the significant improvements in transmission quality achieved by DeepJSCC-MIMO for both open-loop and closed-loop MIMO systems across a wide range of scenarios. Moreover, DeepJSCC-MIMO exhibits robustness to varying channel conditions, channel estimation errors, and different antenna numbers, making it an appealing solution for emerging semantic communication systems.

翻译：本文提出了一种基于视觉Transformer（ViT）的深度联合信源信道编码（DeepJSCC）方案，用于多输入多输出（MIMO）信道上的无线图像传输，该方案被命名为DeepJSCC-MIMO。我们研究了DeepJSCC-MIMO在开环和闭环MIMO系统中的自适应图像传输。这种新颖的DeepJSCC-MIMO架构超越了经典的基于分离的基准方案，对信道估计误差具有鲁棒性，并且在适应不同信道条件和天线数量时展现出显著的灵活性，而无需重新训练。具体而言，通过利用ViT的自注意力机制，DeepJSCC-MIMO能够智能地学习特征映射和功率分配策略，这些策略根据源图像的独特特性和当前信道条件进行定制。大量的数值实验验证了DeepJSCC-MIMO在开环和闭环MIMO系统中，在多种广泛场景下所实现的传输质量的显著提升。此外，DeepJSCC-MIMO对变化的信道条件、信道估计误差以及不同的天线数量均表现出鲁棒性，这使其成为新兴语义通信系统中一种颇具吸引力的解决方案。