Innovative semantic communication system

TL;DR

This paper introduces a novel semantic communication system that integrates AI to transmit and decode semantic information, improving efficiency and robustness in image transmission over noisy channels.

Contribution

It proposes a new semantic communication paradigm that transmits semantic features and decoders, along with a general semantic metric for system quality assessment.

Findings

Effective semantic information extraction and image reconstruction at low SNR.

System performs well over AWGN and multipath fading channels.

Demonstrates feasibility of AI-driven semantic communication for images.

Abstract

Traditional communication systems focus on the transmission process, and the context-dependent meaning has been ignored. The fact that 5G system has approached Shannon limit and the increasing amount of data will cause communication bottleneck, such as the increased delay problems. Inspired by the ability of artificial intelligence to understand semantics, we propose a new communication paradigm, which integrates artificial intelligence and communication, the semantic communication system. Semantic communication is at the second level of communication based on Shannon and Weaver\cite{6197583}, which retains the semantic features of the transmitted information and recovers the signal at the receiver, thus compressing the communication traffic without losing important information. Different from other semantic communication systems, the proposed system not only transmits semantic information but also transmits semantic decoder. In addition, a general semantic metrics is proposed to measure the quality of semantic communication system. In particular, the semantic communication system for image, namely AESC-I, is designed to verify the feasibility of the new paradigm. Simulations are conducted on our system with the additive white Gaussian noise (AWGN) and the multipath fading channel using MNIST and Cifar10 datasets. The experimental results show that DeepSC-I can effectively extract semantic information and reconstruct images at a relatively low SNR.