论文标题
演示：与视觉变压器的实时语义通信
Demo: Real-Time Semantic Communications with a Vision Transformer
论文作者
论文摘要
语义通信有望使含义更有效地传递，而不是符号的精确传递。在本文中，我们提出了一个基于图像传输的端到端深度神经网络体系结构，并通过基于现场可编程的门阵列（FPGA）实现原型来证明其在实时无线通道中的可行性。我们证明，使用流行的CIFAR-10数据集，该系统的表现优于传统的256个二次振幅调制系统。据我们所知，这是实施和调查与视觉变压器实时语义通信的第一部作品。
Semantic communications are expected to enable the more effective delivery of meaning rather than a precise transfer of symbols. In this paper, we propose an end-to-end deep neural network-based architecture for image transmission and demonstrate its feasibility in a real-time wireless channel by implementing a prototype based on a field-programmable gate array (FPGA). We demonstrate that this system outperforms the traditional 256-quadrature amplitude modulation system in the low signal-to-noise ratio regime with the popular CIFAR-10 dataset. To the best of our knowledge, this is the first work that implements and investigates real-time semantic communications with a vision transformer.
