Semantic-Aided Image Transmission System with Unequal Error Protection for Next-Generation Communication Networks

Semantic communication (SC) aims to convey the meaning of data instead of focusing on its bit-by-bit reconstruction. SC finds applications in beyond 5G and 6G networks for artificial intelligence-empowered multimedia content delivery. In this paper, we propose a novel semantic-aided autoencoder-based image transmission system that leverages semantic information in the form of the segmentation map of an image. We demonstrate up to 23% and 18% improvement (in terms of mean square error and peak signal-to-noise ratio, respectively) in the quality of the received image with only 2% extra bandwidth over a traditional autoencoder-based image transmission system. The study also explores channel coding strategies for our proposed system. We focus on the intrinsically robust nature of semantic data, as compared to traditional data, to design low-density parity check code, Hamming code, and polar code-based unequal error protection (UEP) schemes. Comparative evaluations between UEP and equal error protection schemes show that while both approaches yield similar performance, UEP schemes are more efficient.