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arXiv · 2205.03886

Demo: Real-Time Semantic Communications with a Vision Transformer

Hanju Yoo·Taehun Jung·Linglong Dai·Songkuk Kim·Chan-Byoung Chae
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Abstract

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.

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Hanju Yoo, Taehun Jung, Linglong Dai, Songkuk Kim, Chan-Byoung Chae. 2022-05-08. Demo: Real-Time Semantic Communications with a Vision Transformer. https://doi.org/10.1109/iccworkshops53468.2022.9914635

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