Rethinking Modern Communication from Semantic Coding to Semantic Communication

TL;DR

This paper introduces a semantics-aware communication framework that emphasizes conveying meaning over bit accuracy, utilizing semantic encoding, confidence-based distillation, and reinforcement learning to improve communication efficiency and understanding.

Contribution

It proposes a novel semantics-aware communication paradigm with joint semantics-noise coding and RL-powered semantic transmission, addressing limitations of existing methods.

Findings

Enhanced semantic understanding in communication systems

Reduced information overhead compared to traditional methods

Demonstrated benefits of semantics-focused communication

Abstract

Modern communications are usually designed to pursue a higher bit-level precision and fewer bits while transmitting a message. This article rethinks these two major features and introduces the concept and advantage of semantics that characterizes a new kind of semantics-aware communication framework, incorporating both the semantic encoding and the semantic communication problem. After analyzing the underlying defects of existing semantics-aware techniques, we establish a confidence-based distillation mechanism for the joint semantics-noise coding (JSNC) problem and a reinforcement learning (RL)-powered semantic communication paradigm that endows a system the ability to convey the semantics instead of pursuing the bit level accuracy. On top of these technical contributions, this work provides a new insight to understand how the semantics are processed and represented in a semantics-aware coding and communication system, and verifies the significant benefits of doing so. Targeted on the next generation's semantics-aware communication, some critical concerns and open challenges such as the information overhead, semantic security and implementation cost are also discussed and envisioned.