Demo: Real-Time Semantic Communications with a Vision Transformer
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2022
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| _version_ | 1866908995950739456 |
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| author | Yoo, Hanju Jung, Taehun Dai, Linglong Kim, Songkuk Chae, Chan-Byoung |
| author_facet | Yoo, Hanju Jung, Taehun Dai, Linglong Kim, Songkuk Chae, Chan-Byoung |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2205_03886 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Demo: Real-Time Semantic Communications with a Vision Transformer Yoo, Hanju Jung, Taehun Dai, Linglong Kim, Songkuk Chae, Chan-Byoung Signal Processing Artificial Intelligence 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. |
| title | Demo: Real-Time Semantic Communications with a Vision Transformer |
| topic | Signal Processing Artificial Intelligence |
| url | https://arxiv.org/abs/2205.03886 |