Wireless End-to-End Image Transmission System using Semantic Communications

TL;DR

This paper presents a semantic communication system for end-to-end image transmission that leverages AI and GANs to significantly reduce bandwidth usage while maintaining image quality, considering channel distortions.

Contribution

It introduces a novel semantic communication framework using GANs and semantic segmentation for efficient image transmission over wireless channels, highlighting bandwidth savings and robustness.

Findings

Significant bandwidth savings when transmitting semantic maps instead of raw images.

Effective image reconstruction using GANs at the receiver.

Resilience of the system to channel distortions and quantization noise.

Abstract

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.