Rethinking Modern Communication from Semantic Coding to Semantic Communication
Kun Lu, Qingyang Zhou, Rongpeng Li, Zhifeng Zhao, Xianfu Chen, Jianjun, Wu, and Honggang Zhang

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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
