  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.
