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：本文提出一种基于视觉Transformer（ViT）的深度联合源信道编码（DeepJSCC）方案，用于多输入多输出（MIMO）信道上的无线图像传输，称为DeepJSCC-MIMO。我们研究了DeepJSCC-MIMO在开环和闭环MIMO系统中自适应图像传输的应用。新颖的DeepJSCC-MIMO架构超越了经典的基于分离的基准方法，对信道估计误差具有鲁棒性，并在无需重新训练的情况下展现出适应不同信道条件和天线数量的非凡灵活性。具体而言，通过利用ViT的自注意力机制，DeepJSCC-MIMO智能地学习针对源图像独特特征和当前信道条件定制的特征映射与功率分配策略。大量数值实验验证了DeepJSCC-MIMO在开环和闭环MIMO系统中多种场景下所实现的传输质量显著提升。此外，DeepJSCC-MIMO对变化的信道条件、信道估计误差及不同的天线数量均表现出鲁棒性，使其成为新兴语义通信系统中具有吸引力的解决方案。