Neural Coding Is Not Always Semantic: Toward the Standardized Coding Workflow in Semantic Communications

TL;DR

This paper clarifies the concept of semantic coding in communications, proposing a standardized definition and neural coding scheme that emphasizes semantic understanding and general representation for efficient data transmission.

Contribution

It introduces a standardized definition of semantic coding and a neural coding scheme for general semantic representation based on contextual modeling.

Findings

Proposes a standardized definition of semantic coding.

Develops an extensive neural coding scheme for semantic representation.

Shows that minimal modifications enable end-to-end data transmission.

Abstract

Semantic communication, leveraging advanced deep learning techniques, emerges as a new paradigm that meets the requirements of next-generation wireless networks. However, current semantic communication systems, which employ neural coding for feature extraction from raw data, have not adequately addressed the fundamental question: Is general feature extraction through deep neural networks sufficient for understanding semantic meaning within raw data in semantic communication? This article is thus motivated to clarify two critical aspects: semantic understanding and general semantic representation. This article presents a standardized definition on semantic coding, an extensive neural coding scheme for general semantic representation that clearly represents underlying data semantics based on contextual modeling. With these general semantic representations obtained, both human- and machine-centric end-to-end data transmission can be achieved through only minimal specialized modifications, such as fine-tuning and regularization. This article contributes to establishing a commonsense that semantic communication extends far beyond mere feature transmission, focusing instead on conveying compact semantic representations through context-aware coding schemes.