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:语言通信模式旨在弥合现代高容量多媒体应用内容传输中有限带宽问题的差距。将AI技术与6G通信网络结合起来,为开发基于语义通信端至端通信系统铺平了道路。在这项研究中,我们实施了基于语义通信端至端图像传输系统的终端至端通信系统,并讨论了与物理频道特征相结合开发语义通信系统的潜在设计考虑。一个经过预先训练的GAN网络被接收者用作传输任务,以重建基于接收者输入的语义片段图像的真实图像。将AI技术与6G通信网络结合起来,为开发基于语义通信端至端通信的终端通信系统铺平面通信系统铺平了道路。在接收者的GAN网络中,我们实施了基于语义通信端至端图像传输系统的语义通信系统。研究显示,在通过常规通信链路段将数据传输到磁性图像传输系统时,通过常规通信链路段的图像传输系统获取的图像。</s>