# Towards Joint Learning of Optimal MAC Signaling and Wireless Channel Access

arXiv: https://arxiv.org/abs/2007.09948 · HTML: https://mlsanity.com/paper/2007.09948/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2020.

## Built on by (most cited first, 18 of 18)

- [A Bayesian Framework for Digital Twin-Based Control, Monitoring, and Data Collection in Wireless Systems](https://mlsanity.com/paper/2212.01351/) (2022)
- [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)
- [Towards Semantic Communication Protocols for 6G: From Protocol Learning to Language-Oriented Approaches](https://mlsanity.com/paper/2310.09506/) (2023)
- [Learning Generalized Wireless MAC Communication Protocols via Abstraction](https://mlsanity.com/paper/2206.06331/) (2022)
- [Multiple Access in the Era of Distributed Computing and Edge Intelligence](https://mlsanity.com/paper/2403.07903/) (2024)
- [Learning Emergent Random Access Protocol for LEO Satellite Networks](https://mlsanity.com/paper/2112.01765/) (2021)
- [Scalable Joint Learning of Wireless Multiple-Access Policies and their Signaling](https://mlsanity.com/paper/2206.03844/) (2022)
- [LLM-Based Emulation of the Radio Resource Control Layer: Towards AI-Native RAN Protocols](https://mlsanity.com/paper/2505.16821/) (2025)
- [Learning-based Autonomous Channel Access in the Presence of Hidden Terminals](https://mlsanity.com/paper/2207.03605/) (2022)
- [Digital Twin-Based Multiple Access Optimization and Monitoring via Model-Driven Bayesian Learning](https://mlsanity.com/paper/2210.05582/) (2022)
- [Cooperative Multi-Agent Learning for Navigation via Structured State Abstraction](https://mlsanity.com/paper/2306.11336/) (2023)
- [Emergent Communication Protocol Learning for Task Offloading in Industrial Internet of Things](https://mlsanity.com/paper/2401.12914/) (2024)
- [Optimizing Wireless Discontinuous Reception via MAC Signaling Learning](https://mlsanity.com/paper/2406.13834/) (2024)
- [Learning Power Control Protocol for In-Factory 6G Subnetworks](https://mlsanity.com/paper/2505.05967/) (2025)
- [AI-Enhanced Distributed Channel Access for Collision Avoidance in Future Wi-Fi 8](https://mlsanity.com/paper/2509.23154/) (2025)
- [LLM-Empowered Agentic MAC Protocols: A Dynamic Stackelberg Game Approach](https://mlsanity.com/paper/2510.10895/) (2025)
- [Causal Model-Based Reinforcement Learning for Sample-Efficient IoT Channel Access](https://mlsanity.com/paper/2511.10291/) (2025)

## Abstract (arXiv)

Communication protocols are the languages used by network nodes. Before a user equipment (UE) can exchange data with a base station (BS), it must first negotiate the conditions and parameters for that transmission. This negotiation is supported by signaling messages at all layers of the protocol stack. Each year, the mobile communications industry defines and standardizes these messages, which are designed by humans during lengthy technical (and often political) debates. Following this standardization effort, the development phase begins, wherein the industry interprets and implements the resulting standards. But is this massive development undertaking the only way to implement a given protocol? We address the question of whether radios can learn a pre-given target protocol as an intermediate step towards evolving their own. Furthermore, we train cellular radios to emerge a channel access policy that performs optimally under the constraints of the target protocol. We show that multi-agent reinforcement learning (MARL) and learning-to-communicate (L2C) techniques achieve this goal with gains over expert systems. Finally, we provide insight into the transferability of these results to scenarios never seen during training.