Demo: Real-Time Semantic Communications with a Vision Transformer
This work addresses the challenge of more effective meaning delivery in wireless communications, though it is incremental as it builds on existing semantic communication concepts with a new implementation.
The paper tackled the problem of enabling semantic communications for image transmission by proposing an end-to-end deep neural network architecture and implementing a real-time prototype on an FPGA, demonstrating that it outperforms traditional 256-QAM systems in low SNR regimes with the CIFAR-10 dataset.
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.