# Towards Semantic Communication Protocols for 6G: From Protocol Learning to Language-Oriented Approaches

arXiv: https://arxiv.org/abs/2310.09506 · HTML: https://mlsanity.com/paper/2310.09506/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 30 references that are in the map.
- First posted on arXiv 14 Oct 2023.

## Builds on (most cited first, 25 of 25)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Deep Learning and the Information Bottleneck Principle](https://mlsanity.com/paper/1503.02406/) (2015)
- [Learning to Communicate with Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1605.06676/) (2016)
- [Emergence of Grounded Compositional Language in Multi-Agent Populations](https://mlsanity.com/paper/1703.04908/) (2017)
- [Generating Images with Multimodal Language Models](https://mlsanity.com/paper/2305.17216/) (2023)
- [Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications](https://mlsanity.com/paper/2207.09353/) (2022)
- [A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems](https://mlsanity.com/paper/1902.10265/) (2019)
- [A Universal Law of Robustness via Isoperimetry](https://mlsanity.com/paper/2105.12806/) (2021)
- [A Survey of Multi-Agent Deep Reinforcement Learning with Communication](https://mlsanity.com/paper/2203.08975/) (2022)
- [A Latent Space Theory for Emergent Abilities in Large Language Models](https://mlsanity.com/paper/2304.09960/) (2023)
- [Entropic Causal Inference](https://mlsanity.com/paper/1611.04035/) (2016)
- [Towards Joint Learning of Optimal MAC Signaling and Wireless Channel Access](https://mlsanity.com/paper/2007.09948/) (2020)
- [The Emergence of Wireless MAC Protocols with Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2108.07144/) (2021)
- [Towards Semantic Communication Protocols: A Probabilistic Logic Perspective](https://mlsanity.com/paper/2207.03920/) (2022)
- [Minimum--Entropy Couplings and their Applications](https://mlsanity.com/paper/1901.07530/) (2019)
- [Joint Scheduling of URLLC and eMBB Traffic in 5G Wireless Networks](https://mlsanity.com/paper/1712.05344/) (2017)
- [WiThRay: A Versatile Ray-Tracing Simulator for Smart Wireless Environments](https://mlsanity.com/paper/2304.11385/) (2023)
- [Query Age of Information: Freshness in Pull-Based Communication](https://mlsanity.com/paper/2105.06845/) (2021)
- [A Unified Approach to Semantic Information and Communication based on Probabilistic Logic](https://mlsanity.com/paper/2205.00621/) (2022)
- [Attention Based Communication and Control for Multi-UAV Path Planning](https://mlsanity.com/paper/2112.12584/) (2021)
- [On the Similarity between von Neumann Graph Entropy and Structural Information: Interpretation, Computation, and Applications](https://mlsanity.com/paper/2102.09766/) (2021)

## Built on by (most cited first, 7 of 7)

- [TelecomGPT: A Framework to Build Telecom-Specfic Large Language Models](https://mlsanity.com/paper/2407.09424/) (2024)
- [AI-native Interconnect Framework for Integration of Large Language Model Technologies in 6G Systems](https://mlsanity.com/paper/2311.05842/) (2023)
- [Large Generative Model-assisted Talking-face Semantic Communication System](https://mlsanity.com/paper/2411.03876/) (2024)
- [Not All Symbols Are Equal: Importance-Aware Constellation Design for Semantic Communication](https://mlsanity.com/paper/2605.14940/) (2026)
- [Interference-Aware Emergent Random Access Protocol for Downlink LEO Satellite Networks](https://mlsanity.com/paper/2402.02350/) (2024)
- [LLM-Empowered Agentic MAC Protocols: A Dynamic Stackelberg Game Approach](https://mlsanity.com/paper/2510.10895/) (2025)
- [From Semantic to Token Communication: The Next Paradigm for Large-Model-Driven 6G Intelligent Connectivity](https://mlsanity.com/paper/2609.10714/) (2026)

## Abstract (arXiv)

The forthcoming 6G systems are expected to address a wide range of non-stationary tasks. This poses challenges to traditional medium access control (MAC) protocols that are static and predefined. In response, data-driven MAC protocols have recently emerged, offering ability to tailor their signaling messages for specific tasks. This article presents a novel categorization of these data-driven MAC protocols into three levels: Level 1 MAC. task-oriented neural protocols constructed using multi-agent deep reinforcement learning (MADRL); Level 2 MAC. neural network-oriented symbolic protocols developed by converting Level 1 MAC outputs into explicit symbols; and Level 3 MAC. language-oriented semantic protocols harnessing large language models (LLMs) and generative models. With this categorization, we aim to explore the opportunities and challenges of each level by delving into their foundational techniques. Drawing from information theory and associated principles as well as selected case studies, this study provides insights into the trajectory of data-driven MAC protocols and sheds light on future research directions.