Semantic communication is considered the future of mobile communication, which aims to transmit data beyond Shannon's theorem of communications by transmitting the semantic meaning of the data rather than the bit-by-bit reconstruction of the data at the receiver's end. The semantic communication paradigm aims to bridge the gap of limited bandwidth problems in modern high-volume multimedia application content transmission. Integrating AI technologies with the 6G communications networks paved the way to develop semantic communication-based end-to-end communication systems. In this study, we have implemented a semantic communication-based end-to-end image transmission system, and we discuss potential design considerations in developing semantic communication systems in conjunction with physical channel characteristics. A Pre-trained GAN network is used at the receiver as the transmission task to reconstruct the realistic image based on the Semantic segmented image at the receiver input. The semantic segmentation task at the transmitter (encoder) and the GAN network at the receiver (decoder) is trained on a common knowledge base, the COCO-Stuff dataset. The research shows that the resource gain in the form of bandwidth saving is immense when transmitting the semantic segmentation map through the physical channel instead of the ground truth image in contrast to conventional communication systems. Furthermore, the research studies the effect of physical channel distortions and quantization noise on semantic communication-based multimedia content transmission.

翻译：语义通信被视为移动通信的未来发展方向，其目标是通过传输数据的语义含义而非在接收端逐比特重建数据，从而超越香农通信定理实现数据传输。语义通信范式旨在解决现代高容量多媒体应用内容传输中带宽有限的问题。将人工智能技术与第六代通信网络相结合，为开发基于语义通信的端到端通信系统铺平了道路。在本研究中，我们实现了一个基于语义通信的端到端图像传输系统，并讨论了在开发语义通信系统时需结合物理信道特性的潜在设计考量。在接收端使用预训练的生成对抗网络作为传输任务，基于接收输入的语义分割图像重建逼真的图像。发射端（编码器）的语义分割任务与接收端（解码器）的GAN网络均在共同知识库COCO-Stuff数据集上进行训练。研究结果表明，相较于传统通信系统，通过物理信道传输语义分割图而非真实图像时，带宽节省带来的资源增益极为显著。此外，本研究还探究了物理信道失真与量化噪声对基于语义通信的多媒体内容传输的影响。