# Rethinking Modern Communication from Semantic Coding to Semantic Communication

arXiv: https://arxiv.org/abs/2110.08496 · HTML: https://mlsanity.com/paper/2110.08496/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2021.

## Built on by (most cited first, 24 of 24)

- [Less Data, More Knowledge: Building Next Generation Semantic Communication Networks](https://mlsanity.com/paper/2211.14343/) (2022)
- [Digital-SC: Digital Semantic Communication with Adaptive Network Split and Learned Non-Linear Quantization](https://mlsanity.com/paper/2305.13553/) (2023)
- [Reasoning over the Air: A Reasoning-based Implicit Semantic-Aware Communication Framework](https://mlsanity.com/paper/2306.11229/) (2023)
- [Time-Series JEPA for Predictive Remote Control under Capacity-Limited Networks](https://mlsanity.com/paper/2406.04853/) (2024)
- [Semantics-Empowered Communication: A Tutorial-cum-Survey](https://mlsanity.com/paper/2212.08487/) (2022)
- [RALLY: Role-Adaptive LLM-Driven Yoked Navigation for Agentic UAV Swarms](https://mlsanity.com/paper/2507.01378/) (2025)
- [How to Evaluate Semantic Communications for Images with ViTScore Metric?](https://mlsanity.com/paper/2309.04891/) (2023)
- [Deep Learning in Physical Layer: Review on Data Driven End-to-End Communication Systems and their Enabling Semantic Applications](https://mlsanity.com/paper/2401.12800/) (2024)
- [Semantic-aware Digital Twin for Metaverse: A Comprehensive Review](https://mlsanity.com/paper/2305.18304/) (2023)
- [Semantic Edge Computing and Semantic Communications in 6G Networks: A Unifying Survey and Research Challenges](https://mlsanity.com/paper/2411.18199/) (2024)
- [Towards Ubiquitous Semantic Metaverse: Challenges, Approaches, and Opportunities](https://mlsanity.com/paper/2307.06687/) (2023)
- [Multi-level Reliability Interface for Semantic Communications over Wireless Networks](https://mlsanity.com/paper/2407.05487/) (2024)
- [FSSC: Federated Learning of Transformer Neural Networks for Semantic Image Communication](https://mlsanity.com/paper/2407.21507/) (2024)
- [Semantic-Native Communication: A Simplicial Complex Perspective](https://mlsanity.com/paper/2210.16970/) (2022)
- [Alternate Learning based Sparse Semantic Communications for Visual Transmission](https://mlsanity.com/paper/2309.16681/) (2023)
- [LaMI-GO: Latent Mixture Integration for Goal-Oriented Communications Achieving High Spectrum Efficiency](https://mlsanity.com/paper/2412.17839/) (2024)
- [Modeling and Performance Analysis for Semantic Communications Based on Empirical Results](https://mlsanity.com/paper/2504.21055/) (2025)
- [In-Context Source and Channel Coding](https://mlsanity.com/paper/2601.10267/) (2026)
- [Interplay of Semantic Communication and Knowledge Learning](https://mlsanity.com/paper/2402.03339/) (2024)
- [Communication-Efficient Multi-Modal Edge Inference via Uncertainty-Aware Distributed Learning](https://mlsanity.com/paper/2601.14942/) (2026)
- [Internet of Everything in the 6G Era: Paradigms, Enablers, Potentials and Future Directions](https://mlsanity.com/paper/2604.25018/) (2026)
- [Contextual Memory-Enhanced Source Coding for Low-SNR Communications](https://mlsanity.com/paper/2605.04400/) (2026)
- [On Privacy-Preserving Image Transmission in Low-Altitude Networks: A Swin Transformer-Based Framework with Federated Learning](https://mlsanity.com/paper/2605.12566/) (2026)
- [Task-Oriented Candidate-Latent Feedback for Coarse-to-Fine Sensing in Distributed OFDM-ISAC Networks](https://mlsanity.com/paper/2608.03319/) (2026)

## Abstract (arXiv)

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.